Dra. Alma Yolanda Alanis Garcia

RESEÑA

  • Ingeniero Eléctrico, Instituto Tecnológico de Durango, Durango, 2002.
  • Maestría en Ciencias en Ingeniería Eléctrica, CINVESTAV Unidad Guadalajara, Guadalajara, 2004.
  • Doctorado en Ciencias en Ingeniería Eléctrica, CINVESTAV Unidad Guadalajara, Guadalajara, 2007.
  • Miembro SIN (Nivel II).
  • Perfil Deseable PRODEP
  • Líneas de investigación: Modelado y control inteligente y su aplicación a sistemas Automáticos de Control.

TEMA DE TESIS A OFERTAR A ESTUDIANTES

Redes Neuronales artificiales aplicadas a modelado y control de sistemas no lineales inciertos en tiempo discreto.

Breve descripción:

Los sistemas de control modernos por lo general requieren un conocimiento estructurado del sistema a controlar; ese conocimiento suele estar representado en términos de ecuaciones diferenciales o a diferencias. Esta descripción matemática de la dinámica del sistema se denomina modelo. Pueden existir diversos motivos para el establecimiento de modelos matemáticos para sistemas dinámicos, tales como: simulación, predicción, detección de fallas, y el diseño de sistemas de control. Resultados recientes, muestran que las redes neuronales son una herramienta muy efectiva, para el modelado y control de una amplia clase de sistemas no lineales complejos cuando la información del modelo está incompleta. Las redes neuronales presentan un creciente interés debido a su capacidad, de aproximar funciones no lineales; destacan en particular las de alto orden, debido a sus excelentes capacidades de aproximación, requiriendo menos neuronas, y a que son más robustas en presencia de datos variables, ruidosos, con retardos y/o perdida de información. Considerando todo lo anterior es posible plantear diversas investigaciones sobre el desarrollo de esquemas neuronales para modelado y control de sistemas no lineales sujetos a incertidumbres internas (dinámicas no modeladas y variaciones paramétricas), y a perturbaciones externas.

PRODUCCIÓN CIENTÍFICA

 
ARTICULOS
  • Sanchez, O.D., Alanis, A.Y., Ruiz Velázquez, E., Valencia Murillo, R. (2021). Neural identification of Type 1 Diabetes Mellitus for care and forecasting of risk events.Expert Systems with Applications. 183, 115367
  • Uribe, F., Lozada, E., Morales, J., Alanis, A., & Arana-Daniel, N. (2021). A Numerical Technique for Breast Medical Research Based on The FSS Transform. IEEE Latin America Transactions, 19(01), 50–58. https://doi.org/10.1109/tla.2021.9423826
  • Hernandez-Barragan, J., Lopez-Franco, C., Arana-Daniel, N., & Alanis, A. Y. (2021). Inverse kinematics for cooperative mobile manipulators based on self-adaptive differential evolution. PeerJ Computer Science, 7, e419. https://doi.org/10.7717/peerj-cs.419
  • Hernandez-Barragan, J., D. Rios, J., Gomez-Avila, J., Arana-Daniel, N., Lopez-Franco, C., & Alanis, A. Y. (2021). Adaptive neural PD controllers for mobile manipulator trajectory tracking. PeerJ Computer Science, 7, e393. https://doi.org/10.7717/peerj-cs.393
  • Alanis, A. Y., & Alvarez, J. G. (2021). Real‐time model‐free resilient control for discrete nonlinear systems. Asian Journal of Control. Published. https://doi.org/10.1002/asjc.2564
  • Rios, J. D., Alanis, A. Y., Arana‐Daniel, N., & Lopez‐Franco, C. (2020). Real‐time neural observer‐based controller for unknown nonlinear discrete delayed systems. International Journal of Robust and Nonlinear Control, 30(18), 8402–8429. https://doi.org/10.1002/rnc.5250
  • Alma Y., A., Gustavo, M. G., & Jorge, R. (2020). Super-twisting Speed Control of a Brushless Direct Current Motor with Back-EMF. IEEE Latin America Transactions, 18(12), 2055–2062. https://doi.org/10.1109/tla.2020.9400432
  • Rios, Y., García-Rodríguez, J., Sánchez, E., Alanis, A., Ruiz-Velázquez, E., & Pardo, A. (2020). Control neuro-fuzzy para páncreas artificial: desarrollo y validación in-silico. Revista Iberoamericana de Automática e Informática industrial, 17(4), 390. https://doi.org/10.4995/riai.2020.13035
  • "Alanis, A.Y, E.A. Hernandez-Vargas, N.F Ramirez, D. Rios-Rivera. (2020). Neural Control for Epidemic Model of COVID-19 with a Complex Network Approach. IEEE Latin America Transactions. 100 (1e): 1-8"
  • Alanis, A. Y., Rios, J. D., Arana-Daniel, N., & Lopez-Franco, C. (2020). Real-time neural control of all-terrain tracked robots with unknown dynamics andnetwork communication delays. Ingeniería Investigación y Tecnología, 21(3), 1–12. https://doi.org/10.22201/fi.25940732e.2020.21.3.026
  • Martinez-Soltero, G., Alanis, A. Y., Arana-Daniel, N., & Lopez-Franco, C. (2020). Semantic Segmentation for Aerial Mapping. Mathematics, 8(9), 1456. https://doi.org/10.3390/math8091456
  • Ríos-Rivera, D., Alanis, A. Y., & Sanchez, E. N. (2020). Neural-Impulsive Pinning Control for Complex Networks Based on V-Stability. Mathematics, 8(9), 1388. https://doi.org/10.3390/math8091388
  • Alanis, A. Y., Rios, J. D., Gomez-Avila, J., Zuniga, P., & Jurado, F. (2020). Discrete-Time Neural Control of Quantized Nonlinear Systems with Delays: Applied to a Three-Phase Linear Induction Motor. Electronics, 9(8), 1274. https://doi.org/10.3390/electronics9081274
  • Alanis, A. Y., Munoz-Gomez, G., & Rivera, J. (2020). Nested High Order Sliding Mode Controller with Back-EMF Sliding Mode Observer for a Brushless Direct Current Motor. Electronics, 9(6), 1041. https://doi.org/10.3390/electronics9061041
  • Hernandez-Barragan, J., Rios, J. D., Alanis, A. Y., Lopez-Franco, C., Gomez-Avila, J., & Arana-Daniel, N. (2020). Adaptive Single Neuron Anti-Windup PID Controller Based on the Extended Kalman Filter Algorithm. Electronics, 9(4), 636. https://doi.org/10.3390/electronics9040636
  • Barrios-dV, S., Lopez-Franco, M., Rios, J. D., Arana-Daniel, N., Lopez-Franco, C., & Alanis, A. Y. (2020). An Autonomous Path Controller in a System on Chip for Shrimp Robot. Electronics, 9(3), 441. https://doi.org/10.3390/electronics9030441
  • Gomez-Avila, J., Villaseñor, C., Hernandez-Barragan, J., Arana-Daniel, N., Alanis, A. Y., & Lopez-Franco, C. (2020). Neural PD Controller for an Unmanned Aerial Vehicle Trained with Extended Kalman Filter. Algorithms, 13(2), 40. https://doi.org/10.3390/a13020040
  • Alanis, A. Y., Rios-Huerta, D., Rios, J. D., Arana-Daniel, N., Lopez-Franco, C., & Sanchez, E. N. (2020). High-Order Sliding Modes Based On-Line Training Algorithm for Recurrent High-Order Neural Networks. IFAC-PapersOnLine, 53(2), 8187–8192. https://doi.org/10.1016/j.ifacol.2020.12.2320
  • Camacho, J., Villaseñor, C., Alanis, A. Y., Lopez-Franco, C., & Arana-Daniel, N. (2020). sKAdam: An improved scalar extension of KAdam for function optimization. Intelligent Data Analysis, 24, 87–104. https://doi.org/10.3233/ida-200010
  • Rios, Y., García-Rodríguez, J., Sánchez, E., Alanis, A., Ruiz-Velázquez, E., & Pardo, A. (2020). Control neuro-fuzzy para páncreas artificial: desarrollo y validación in-silico. Revista Iberoamericana de Automática e Informática industrial, 17(4), 390. https://doi.org/10.4995/riai.2020.13035
  • Suarez, O. J., Vega, C. J., Sanchez, E. N., González-Santiago, A. E., Rodríguez-Jorge, O., Alanis, A. Y., Chen, G., & Hernandez-Vargas, E. A. (2020). Pinning Control for the p53-Mdm2 Network Dynamics Regulated by p14ARF. Frontiers in Physiology, 11. https://doi.org/10.3389/fphys.2020.00976
  • Hernandez-Mejia, G., Alanis, A. Y., Hernandez-Gonzalez, M., Findeisen, R., & Hernandez-Vargas, E. A. (2020). Passivity-Based Inverse Optimal Impulsive Control for Influenza Treatment in the Host. IEEE Transactions on Control Systems Technology, 28(1), 94–105. https://doi.org/10.1109/tcst.2019.2892351
  • Hernandez-Mejia, G., Alanis, A. Y., Hernandez-Gonzalez, M., Findeisen, R., & Hernandez-Vargas, E. A. (2020). Passivity-Based Inverse Optimal Impulsive Control for Influenza Treatment in the Host. IEEE Transactions on Control Systems Technology, 28(1), 94–105. https://doi.org/10.1109/tcst.2019.2892351
  • Hernández-Barragán, J., López-Franco, C., Alanis, A. Y., Arana-Daniel, N., & López-Franco, M. (2019). Dual-arm cooperative manipulation based on differential evolution. International Journal of Advanced Robotic Systems, 16(1), 172988141882518. https://doi.org/10.1177/1729881418825188
  • Sánchez, O. D., Ruiz‐Velázquez, E., Alanís, A. Y., Quiroz, G., & Torres‐Treviño, L. (2019). Parameter estimation of a meal glucose–insulin model for TIDM patients from therapy historical data. IET Systems Biology, 13(1), 8–15. https://doi.org/10.1049/iet-syb.2018.5038
  • Hernandez-Vargas, E. A., Alanis, A. Y., & Tetteh, J. (2019). A new view of multiscale stochastic impulsive systems for modeling and control of epidemics. Annual Reviews in Control, 48, 242–249. https://doi.org/10.1016/j.arcontrol.2019.06.002
  • Sánchez, O. D., Ruiz-Velázquez, E., Alanís, A. Y., Quiroz, G., & Torres-Treviño, L. (2018). Parameter estimation of a meal glucose–insulin model for TIDM patients from therapy historical data. IET systems biology, 13(1), 8-15.
  • Alanis, A. Y., & Hernandez-Vargas, E. A. (2019). Special section on modeling, identification and control of nonlinear systems.
  • Hernandez-Vargas, E. A., Alanis, A. Y., & Tetteh, J. (2019). A new view of multiscale stochastic impulsive systems for modeling and control of epidemics. Annual Reviews in Control.
  • Hernández-Barragán, J., López-Franco, C., Alanis, A. Y., Arana-Daniel, N., & López-Franco, M. (2019). Dual-arm cooperative manipulation based on differential evolution. International Journal of Advanced Robotic Systems, 16(1), 1729881418825188.
  • Hernandez-Mejia, G., Alanis, A. Y., & Hernandez-Vargas, E. A. (2018). Neural inverse optimal control for discrete-time impulsive systems. Neurocomputing, 314, 101-108.
  • Villaseñor, C., Arana-Daniel, N., Alanis, A. Y., López-Franco, C., & Hernandez-Vargas, E. A. (2018). Germinal center optimization algorithm. International Journal of Computational Intelligence Systems, 12(1), 13-27.
  • Lopez-Franco, C., Hernandez-Barragan, J., Alanis, A. Y., & Arana-Daniel, N. (2018). A soft computing approach for inverse kinematics of robot manipulators. Engineering Applications of Artificial Intelligence, 74, 104-120.
  • Villaseñor, C., Arana-Daniel, N., Alanis, A. Y., Lopez-Franco, C., & Gomez-Avila, J. (2018). Multiellipsoidal Mapping Algorithm. Applied Sciences, 8(8), 1239.
  • Villaseñor, C., Gutierrez-Frias, E. F., Arana-Daniel, N., Alanis, A. Y., & Lopez-Franco, C. (2018). Parallel crossed chaotic encryption for hyperspectral images. Applied Sciences, 8(7), 1183.
  • Arana-Daniel, N., Villaseñor, C., López-Franco, C., Alanís, A. Y., & Valencia-Murillo, R. (2017). Structure from Motion Using Bio-Inspired Intelligence Algorithm and Conformal Geometric Algebra. Intelligent Automation & Soft Computing, 1-7.
  • López-Franco, C., Gomez-Avila, J., Arana-Daniel, N., & Alanis, A. Y. (2014, August). Robot pose estimation based on visual information and particle swarm optimization. In 2014 World Automation Congress (WAC) (pp. 768-773). IEEE.
  • Alanis, A. Y. (2018). Electricity prices forecasting using artificial neural networks. IEEE Latin America Transactions, 16(1), 105-111.
  • Munoz-Gomez, G., Alanis, A. Y., & Rivera, J. (2018). Nested High Order Sliding Mode Controller Applied to a Brushless Direct Current Motor. IFAC-PapersOnLine, 51(13), 174-179.
  • Vega-Magdaleno, G. D., Alanis, A. Y., Hernandez-Mejia, G., & Hernandez-Vargas, E. A. (2018). Impulsive MPC for influenza infection treatment at variable time. IFAC-PapersOnLine, 51(13), 79-84.
  • Torres-Cerna, C. E., Morales, J. A., Alanis, A. Y., & Hernandez-Vargas, E. A. (2018). Discrete-time neural network identification of quorum sensing escherichia coli regulators. IFAC-PapersOnLine, 51(13), 120-124.
  • Rios, J. D., Alanis, A. Y., Lopez-Franco, C., & Arana-Daniel, N. (2018). RHONN identifier-control scheme for nonlinear discrete-time systems with unknown time-delays. Journal of the Franklin Institute, 355(1), 218-249.
  • Lopez-Franco, A., Alanis, A. Y., Lopez-Franco, C., Arana-Daniel, N., & Lopez-Franco, M. (2018). Emotional system in complex cognitive activities of working memory: A literature review of its role. Journal of integrative neuroscience, 17(3-4), 679-693.
  • Gomez-Avila, J., Lopez-Franco, C., Alanis, A. Y., Arana-Daniel, N., & Lopez-Franco, M. (2018). Ground Vehicle Tracking with a Quadrotor using Image Based Visual Servoing. IFAC-PapersOnLine, 51(13), 344-349.
  • Antonio-Toledo, M. E., Sanchez, E. N., Alanis, A. Y., Florez, J. A., & Perez-Cisneros, M. A. (2018). Real-time integral backstepping with sliding mode control for a quadrotor UAV. IFAC-PapersOnLine, 51(13), 549-554.
  • Rios, J. D., Villaseñor, C., Alanis, A. Y., Arana-Daniel, N., & Lopez-Franco, C. (2018). Germinal center optimization applied to recurrent high order neural network observer. IFAC-PapersOnLine, 51(13), 332-337.
  • López-Franco, C., Hernández-Barragán, J., Alanis, A. Y., Arana-Daniel, N., & López-Franco, M. (2018). Inverse kinematics of mobile manipulators based on differential evolution. International Journal of Advanced Robotic Systems, 15(1), 1729881417752738.
  • Villaseñor, C., Rios, J. D., Arana-Daniel, N., Alanis, A. Y., Lopez-Franco, C., & Hernandez-Vargas, E. A. (2018). Germinal center optimization applied to neural inverse optimal control for an all-terrain tracked robot. Applied Sciences, 8(1), 31.
  • Rios, J. D., Alanis, A. Y., Arana-Daniel, N., & Lopez-Franco, C. (2017). Recurrent high order neural observer for discrete-time non-linear systems with unknown time-delay. Neural Processing Letters, 46(2), 663-679.
  • Lopez-Franco, C., Gomez-Avila, J., Alanis, A. Y., Arana-Daniel, N., & Villaseñor, C. (2017). Visual servoing for an autonomous hexarotor using a neural network based PID controller. Sensors, 17(8), 1865.
  • Hernandez-Mejia, G., Alanis, A. Y., & Hernandez-Vargas, E. A. (2017). Inverse optimal impulsive control based treatment of influenza infection. IFAC-PapersOnLine, 50(1), 12185-12190.
  • Lopez, V. G., Sanchez, E. N., Alanis, A. Y., & Rios, J. D. (2017). Real-time neural inverse optimal control for a linear induction motor. International Journal of Control, 90(4), 800-812.
  • Torres-Cerna, C. E., Alanis, A. Y., Poblete-Castro, I., & Hernandez-Vargas, E. A. (2017). Batch cultivation model for biopolymer production. Chemical and biochemical engineering quarterly, 31(1), 89-99.
  • Rios, J. D., Alanis, A. Y., Lopez-Franco, M., Lopez-Franco, C., & Arana-Daniel, N. (2017). Real-time neural identification and inverse optimal control for a tracked robot. Advances in Mechanical Engineering, 9(3), 1687814017692970.
  • Arana-Daniel, N., Gallegos, A. A., López-Franco, C., Alanís, A. Y., Morales, J., & López-Franco, A. (2016). Support vector machines trained with evolutionary algorithms employing kernel adatron for large scale classification of protein structures. Evolutionary Bioinformatics, 12, EBO-S40912.
  • Alanis, A. Y., Rios, J. D., Arana-Daniel, N., & Lopez-Franco, C. (2016). Neural identifier for unknown discrete-time nonlinear delayed systems. Neural Computing and Applications, 27(8), 2453-2464.
  • Lastire, E. A., Sanchez, E. N., Alanis, A. Y., & Ornelas-Tellez, F. (2016). Passivity analysis of discrete-time inverse optimal control for trajectory tracking. Journal of the Franklin Institute, 353(13), 3192-3206.
  • Alanis, A. Y. (2016). Real-time Implementation of a Discrete Reduced Order Neural Observer: Linear Induction Motors Application. Intelligent Automation & Soft Computing, 22(3), 491-498.
  • Lopez-Franco, M., Sanchez, E. N., Alanis, A. Y., & López-Franco, C. (2016). Neural control for driving a mobile robot integrating stereo vision feedback. Neural Processing Letters, 43(2), 425-444.
  • Lopez-Franco, M., Sanchez, E. N., Alanis, A. Y., Lopez-Franco, C., & Arana-Daniel, N. (2015). Decentralized control for stabilization of nonlinear multi-agent systems using neural inverse optimal control. Neurocomputing, 168, 81-91.
  • Alanis, A. Y., Arana-Daniel, N., & Lopez-Franco, C. (2015). Bacterial foraging optimization algorithm to improve a discrete-time neural second order sliding mode controller. Applied Mathematics and Computation, 271, 43-51.
  • Vázquez, L. A., Jurado, F., & Alanís, A. Y. (2015). Decentralized identification and control in real-time of a robot manipulator via recurrent wavelet first-order neural network. Mathematical Problems in Engineering, 2015.
  • Alanis, A. Y., Arana-Daniel, N., Lopez-Franco, C., & Guevara-Reyes, E. (2015). Integration of an Inverse Optimal Neural Controller with Reinforced-SLAM for Path Panning and Mapping in Dynamic Environments. Computación y Sistemas, 19(3), 445-456.
  • Lopez-Franco, M., Sanchez, E. N., Alanis, A. Y., & López-Franco, C. (2015). Neural control for a differential drive wheeled mobile robot integrating stereo vision feedback. Computación y Sistemas, 19(3), 429-443.
  • Guillermo, J. E., Castellanos, L. J. R., Sanchez, E. N., & Alanis, A. Y. (2015). Detection of heart murmurs based on radial wavelet neural network with Kalman learning. Neurocomputing, 164, 307-317.
  • Lopez, V. G., Alanis, A. Y., Sanchez, E. N., & Rivera, J. (2015). Real-time implementation of neural optimal control and state estimation for a linear induction motor. Neurocomputing, 152, 403-412.
  • Alanis, A. Y., Rios, J. D., Rivera, J., Arana-Daniel, N., & Lopez-Franco, C. (2015). Real-time discrete neural control applied to a Linear Induction Motor. Neurocomputing, 164, 240-251.
  • López-Franco, C., López-Franco, M., Alanis, A. Y., Gómez-Avila, J., & Arana-Daniel, N. (2015). Real-time inverse optimal neural control for image based visual servoing with nonholonomic mobile robots. Mathematical Problems in Engineering, 2015.
  • Gurubel, K. J., Alanis, A. Y., Sánchez, E. N., & Carlos-Hernandez, S. (2014). A neural observer with time-varying learning rate: analysis and applications. International journal of neural systems, 24(01), 1450011.
  • Alanis, A. Y., Hernandez-Gonzalez, M., & Hernandez-Vargas, E. A. (2014). Observers for biological systems. Applied Soft Computing, 24, 1175-1182.
  • Alanis, A. Y., Sanchez, E. N., & Loukianov, A. G. (2014). Real-time output trajectory tracking neural sliding mode controller for induction motors. Journal of the Franklin Institute, 351(4), 2315-2334.
  • López-Franco, C., Villavicencio, L., Arana-Daniel, N., & Alanis, A. Y. (2014). Image classification using PSO-SVM and an RGB-D sensor. Mathematical Problems in Engineering, 2014.
  • Alanis, A. Y., Lastire, E. A., Arana-Daniel, N., & Lopez-Franco, C. (2014). Inverse optimal control with speed gradient for a power electric system using a neural reduced model. Mathematical Problems in Engineering, 2014.
  • Leon, B. S., Alanis, A. Y., Sanchez, E. N., Ornelas-Tellez, F., & Ruiz-Velazquez, E. (2014). Neural Inverse Optimal Control via Passivity for Subcutaneous Blood Glucose Regulation in Type 1 Diabetes Mellitus Patients. Intelligent Automation & Soft Computing, 20(2), 279-295.
  • Alanis, A. Y., Ricalde, L. J., Simetti, C., & Odone, F. (2013). Neural model with particle swarm optimization Kalman learning for forecasting in smart grids. Mathematical Problems in Engineering, 2013.
  • López-Franco, C., Arana-Daniel, N., & Alanis, A. Y. (2013). Visual servoing on the sphere using conformal geometric algebra. Advances in Applied Clifford Algebras, 23(1), 125-141.
  • Alanis, A. Y., Ornelas-Tellez, F., & Sanchez, E. N. (2013). Discrete-time inverse optimal neural control for synchronous generators. Engineering Applications of Artificial Intelligence, 26(2), 697-705.
  • Alanis, A. Y., Rangel, E., Rivera, J., Arana-Daniel, N., & Lopez-Franco, C. (2013). Particle swarm based approach of a real-time discrete neural identifier for linear induction motors. Mathematical Problems in Engineering, 2013.
  • Hernandez-Gonzalez, M., Alanis, A. Y., & Hernandez-Vargas, E. A. (2012). Decentralized discrete-time neural control for a Quanser 2-DOF helicopter. Applied Soft Computing, 12(8), 2462-2469.
  • Alanis, A. Y., Lopez‐Franco, M., Arana‐Daniel, N., & Lopez‐Franco, C. (2012). Discrete‐time neural control for electrically driven nonholonomic mobile robots. International Journal of adaptive control and signal processing, 26(7), 630-644.
  • Leon, B. S., Alanis, A. Y., Sanchez, E. N., Ruiz‐Velazquez, E., & Ornelas‐Tellez, F. (2012). Inverse optimal neural control for a class of discrete‐time nonlinear positive systems. International Journal of Adaptive Control and Signal Processing, 26(7), 614-629.
  • Leon, B. S., Alanis, A. Y., Sanchez, E. N., Ornelas-Tellez, F., & Ruiz-Velazquez, E. (2012). Inverse optimal neural control of blood glucose level for type 1 diabetes mellitus patients. Journal of the Franklin Institute, 349(5), 1851-1870.
  • Pérez-Cruz, J. H., Alanis, A. Y., Rubio, J. D. J., & Pacheco, J. (2012). System identification using multilayer differential neural networks: a new result. Journal of Applied Mathematics, 2012.
  • Alanis, A. Y., Leon, B. S., Sanchez, E. N., & Ruiz-Velazquez, E. (2011). Blood glucose level neural model for type 1 diabetes mellitus patients. International journal of neural systems, 21(06), 491-504.
  • Alanis, A. Y., Sanchez, E. N., & Loukianov, A. G. (2010). Real-time discrete backstepping neural control for induction motors. IEEE Transactions on control systems technology, 19(2), 359-366.
  • Alanis, A. Y., Sanchez, E. N., Loukianov, A. G., & Perez, M. A. (2011). Real-time recurrent neural state estimation. IEEE Transactions on Neural Networks, 22(3), 497-505.
  • Alanis, A. Y., Sanchez, E. N., Loukianov, A. G., & Hernandez, E. A. (2010). Discrete-time recurrent high order neural networks for nonlinear identification. Journal of the Franklin Institute, 347(7), 1253-1265.
  • Alanis, A. Y., Sanchez, E. N., & Ricalde, L. J. (2010). Discrete-time reduced order neural observers for uncertain nonlinear systems. International journal of neural systems, 20(01), 29-38.
  • Alanis, A. Y., Sanchez, E. N., Loukianov, A. G., & Perez-Cisneros, M. A. (2009). Real-time discrete neural block control using sliding modes for electric induction motors. IEEE Transactions on Control Systems Technology, 18(1), 11-21.
  • Alanis, A. Y., Sanchez, E. N., & Loukianov, A. G. (2007). Discrete-time adaptive backstepping nonlinear control via high-order neural networks. IEEE Transactions on Neural Networks, 18(4), 1185-1195.
 
LIBROS
  • Lopez-Franco, M., Sanchez, E. N., Alanis, A. Y., Lopez-Franco, C., & Arana-Daniel, N. (2020). Discrete-Time Decentralized Inverse Optimal Higher Order Neural Network Control for a Multi-Agent Omnidirectional Mobile Robot. Deep Learning and Neural Networks, 1155–1174. https://doi.org/10.4018/978-1-7998-0414-7.ch064
  • Alanis, A. Y., Arana-Daniel, N., & Lopez-Franco, C. (2019). Artificial Neural Networks for Engineering Applications. Elsevier Gezondheidszorg.
  • Arana-Daniel, N., Lopez-Franco, C., & Alanis, A. Y. (2018). Bio-inspired algorithms for engineering. Butterworth-Heinemann.
  • Alanis, A. Y., & Sanchez, E. N. (2017). Discrete-Time Neural Observers: Analysis and Applications. Academic Press.
  • Garcia-Hernandez, R., Lopez-Franco, M., Sanchez, E. N., Alanis, A. Y., & Ruz-Hernandez, J. A. (2017). Decentralized Neural Control: Application to Robotics (Vol. 96). Springer.
  • Sanchez, E. N., Alanís, A. Y., & Loukianov, A. G. (2008). Discrete-time high order neural control. Warsaw: Springer.
  • E. N. Sánchez and A. Y. Alanis, Redes neuronales, Pearson educacion (in spanish), pp. 232, July, 2006. ISBN:848322295.
 
CAPITULOS DE LIBROS
  • Alanis, A. Y., Arana-Daniel, N., Lopez-Franco, C., & Rios, J. D. (2020). Neural Evolutionary Predictive Control for Linear Induction Motors with Experimental Data. Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications, 373–389. https://doi.org/10.1007/978-3-030-35445-9_28
  • Alanis, A. Y., Rios, Y., García-Rodríguez, J., Sanchez, E. N., Ruiz-Velázquez, E., & Garcia, A. P. (2020). Neuro-fuzzy inverse optimal control incorporating a multistep predictor as applied to T1DM patients. Control Applications for Biomedical Engineering Systems, 1–24. https://doi.org/10.1016/b978-0-12-817461-6.00001-9
  • Villaseñor C., Arana-Daniel N., Alanis A.Y., Lopez-Franco C., Valencia-Murillo R. (2020) Tracking of Non-rigid Motion in 3D Medical Imaging with Ellipsoidal Mapping and Germinal Center Optimization. In: Castillo O., Melin P. (eds) Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine. Studies in Computational Intelligence, vol 827. Springer, Cham. https://doi.org/10.1007/978-3-030-34135-0_17
  • Villaseñor,C., Gomez-Avila, J., Arana-Daniel, N. Alanis, A. Y., Lopez-Franco, C. (2019) Fast Chaotic Encryption for Hyperspectral Images. In: Chen, J., Song, Y., & Li, H. Processing and Analysis of Hyperspectral Data. IntechOpen.
  • Soltero, E. G. M., Lopéz-Franco, C., Alanis, A. Y., & Arana-Daniel, N. (2017, October). Outdoor Robot Navigation Based on Particle Swarm Optimization. In North American Fuzzy Information Processing Society Annual Conference (pp. 225-231). Springer, Cham.
  • Rios, J. D., Alanís, A. Y., Arana-Daniel, N., & López-Franco, C. (2017, October). Neural Identifier-Control Scheme for Nonlinear Discrete Systems with Input Delay. In North American Fuzzy Information Processing Society Annual Conference (pp. 242-247). Springer, Cham.
  • Alanis, A. Y., Hernandez-Vargas, E. A., Chen, G., Sanchez, E. N., Hong, Y., & Čelikovský, S. (2018). Special issue on new trends on complex systems: Adaptation and control. Kybernetika, 54(1), 1-2.
  • Sanchez, E. N., Alanis, A. Y., Lopez-Franco, M., Arana-Daniel, N., & Lopez-Franco, C. (2015). Real-time neural control of mobile robots. In Control and Systems Engineering (pp. 205-229). Springer, Cham.
  • Alanis, A. Y., & Ruz-Hernandez, J. (2014). Special Section on Advances in Intelligent Control: Theory and Applications.
  • Ricalde, L. J., Catzin, G. A., Alanis, A. Y., & Sanchez, E. N. (2013). Time Series Forecasting via a Higher Order Neural Network trained with the Extended Kalman Filter for Smart Grid Applications. In Artificial Higher Order Neural Networks for Modeling and Simulation (pp. 254-274). IGI Global.
  • Lopez-Franco, M., Alanis, A. Y., Arana-Daniel, N., & Lopez-Franco, C. (2013). Artificial Higher Order Neural Networks for Modeling MIMO Discrete-Time Nonlinear System. In Artificial Higher Order Neural Networks for Modeling and Simulation (pp. 30-43). IGI Global.
  • Ricalde, L. J., Sanchez, E. N., & Alanis, A. Y. (2010). Recurrent higher order neural network control for output trajectory tracking with neural observers and constrained inputs. In Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications (pp. 286-311). IGI Global.
  • Sanchez, E. N., Urrego, D. V., Alanis, A. Y., & Carlos-Hernandez, S. (2010). Recurrent higher order neural observers for anaerobic processes. In Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications (pp. 333-365). IGI Global.
  • Sanchez, E. N., Alanis, A. Y., & Rico, J. (2009). Electric Load Demand and Electricity Prices ForecastingUsing Higher Order Neural Networks Trained by Kalman Filtering. In Artificial Higher Order Neural Networks for Economics and Business (pp. 295-313). IGI Global.
 
PARTICIPACIONES EN CONGRESOS
  • Mojica, F., Villasenor, C., Alanis, A. Y., & Arana-Daniel, N. (2019). Long Short-Term Memory with Smooth Adaptation. 2019 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). Published. https://doi.org/10.1109/ropec48299.2019.9057098
  • Camacho, J. D., Villaseñor, C., Alanis, A. Y., Lopez-Franco, C., & Arana-Daniel, N. (2019). KAdam: Using the Kalman Filter to Improve Adam algorithm. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 429–438. https://doi.org/10.1007/978-3-030-33904-3_40
  • Hernandez–Gonzalez, M., Hernandez-Mejia, G., Alanis, A. Y., & Hernandez-Vargas, E. A. (2019). State Estimation for Stochastic Nonlinear Systems with Applications to Viral Infections. 2019 18th European Control Conference (ECC). Published. https://doi.org/10.23919/ecc.2019.8795918
  • Antonio-Toledo, M. E., Sanchez, E. N., & Alanis, A. Y. (2018, November). Neural Inverse Optimal Control Applied to Quadrotor UAV. In 2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI) (pp. 1-8). IEEE.
  • Martinez-Soltero, E. G., Lopez-Franco, C., Alanis, A. Y., & Arana-Daniel, N. (2018, November). Bio-inspired Algorithm for Path Planning of Terrestrial Robot Using Aerial Images. In 2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI) (pp. 1-5). IEEE.
  • Gómez-Avila, J., López-Franco, C., Alanis, A. Y., & Arana-Daniel, N. (2018, November). Control of Quadrotor using a Neural Network based PID. In 2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI) (pp. 1-6). IEEE.
  • Hernandez-Barragan, J., Lopez-Franco, C., Antonio-Gopar, C., Alanis, A. Y., & Arana-Daniel, N. (2018, November). The inverse kinematics solutions for robot manipulators based on firefly algorithm. In 2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI) (pp. 1-5). IEEE.
  • Antonio-Gopar, L. C., Lopez-Franco, C., Arana-Daniel, N., Gonzalez-Vallejo, E., & Alanis, A. Y. (2018, November). Inverse Kinematics for a Manipulator Robot based on Differential Evolution Algorithm. In 2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI) (pp. 1-5). IEEE.
  • Rios, Y. Y., García-Rodríguez, J. A., Sanchez, E. N., Alanis, A. Y., & Ruiz-Velázquez, E. (2018, November). Rapid Prototyping of Neuro-Fuzzy Inverse Optimal Control as Applied to T1DM Patients. In 2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI) (pp. 1-5). IEEE.
  • Covarrubias, R., Alanis, A. Y., Rios, D., Sanchez, E. N., & Hernandez-Vargas, E. A. (2018, November). Discrete-Time Neural Identification of a SIR Epidemic Model. In 2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI) (pp. 1-6). IEEE.
  • Bautista, S. M. V., Eduardo, R. C., Cisneros, M. A. P., Ricalde, L. J., Gomez, M. A. O., Alanis, A. Y., & Jimenez, L. A. (2018, November). Wind Speed Time Series Forecasting Using a Neural Network Model Inspired Biologically. In 2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI) (pp. 1-5). IEEE.
  • Camacho, J. D., Villaseñor, C., Alanis, A. Y., Lopez-Franco, C., & Arana-Daniel, N. (2019, October). KAdam: Using the Kalman Filter to Improve Adam algorithm. In Iberoamerican Congress on Pattern Recognition (pp. 429-438). Springer, Cham.
  • Sanchez, E. N., Covarrubias, R., Alanis, A. Y., & Hernandez-Vargas, E. A. (2018, September). Inverse Optimal Impulsive Control for a SIR Epidemic Model. In 2018 15th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) (pp. 1-6). IEEE.
  • Hernandez-Mejia, G., Hernandez-Vargas, E. A., Alanis, A. Y., & Arana-Daniel, N. (2018, July). Recurrent High Order Neural Networks Identification for Infectious Diseases. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE.
  • Villaseñor, C., Arana-Daniel, N., Alanis, A. Y., & Lopez-Franco, C. (2018, July). Differential evolution and covariance ellipsoid for non-rigid transformation tracking of internal organs. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE.
  • Rios, Y. Y., García-Rodríguez, J. A., Sánchez, O. D., Sanchez, E. N., Alanis, A. Y., Ruiz-Velázquez, E., & Arana-Daniel, N. (2018, July). Inverse optimal control using a neural multi-step predictor for T1DM treatment. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
  • Alanis, A. Y., Arana-Daniel, N., Lopez-Franco, C., Perez-Cisneros, M. A., & Sanchez, E. N. (2017, November). PSO for parametric identification of rotatory induction motors using experimental data with unknown time-delays. In 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI) (pp. 1-6). IEEE.
  • Djilali, L., Suarez, O. J., Sanchez, E. N., Alanis, A. Y., & Garcia, A. P. (2017, November). Real-time neural backstepping control for a helicopter prototype. In 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI) (pp. 1-6). IEEE.
  • Arana-Daniel, N., Valdés-López, J., Alanís, A. Y., & López-Franco, C. (2017, November). Traversability Cost Identification of Dynamic Environments Using Recurrent High Order Neural Networks for Robot Navigation. In Iberoamerican Congress on Pattern Recognition (pp. 61-68). Springer, Cham.
  • Villaseñor, C., Arana-Daniel, N., Alanis, A. Y., & Lopez-Franco, C. (2017, May). Hyperellipsoidal neuron. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 788-794). IEEE.
  • Torres-Cerna, C. E., Alanis, A. Y., Poblete-Castro, I., Bermejo-Jambrina, M., & Hernandez-Vargas, E. A. (2016, July). A comparative study of differential evolution algorithms for parameter fitting procedures. In 2016 IEEE Congress on Evolutionary Computation (CEC) (pp. 4662-4666). IEEE.
  • Antonio-Toledo, M. E., Sanchez, E. N., & Alanis, A. Y. (2016, July). Robust neural decentralized control for a quadrotor UAV. In 2016 International Joint Conference on Neural Networks (IJCNN) (pp. 714-719). IEEE.
  • Lastire-Olmedo, E. A., Sanchez, E. N., Alanis, A. Y., & Ornelas-Tellez, F. (2016, July). Robustness of discrete-time inverse optimal control for trajectory tracking. In 2016 World Automation Congress (WAC) (pp. 1-6). IEEE.
  • Hernandez, T., Nuno, E., & Alanis, A. Y. (2016, April). Teleoperation of mobile manipulators with non-holonomic restrictions. In 2016 IEEE 13th International Conference on Networking, Sensing, and Control (ICNSC) (pp. 1-6). IEEE.
  • Rios, J. D., Alanis, A. Y., Arana-Daniel, N., & Lopez-Franco, C. (2015, November). RHONN identifier for unknown nonlinear discrete-time delay systems. In 2015 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) (pp. 1-5). IEEE.
  • Ramirez, J., Castro, A., Zuniga, P., & Alanis, A. Y. (2015, November). High order Sliding Mode Control for shunt Active Power Filter. In 2015 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) (pp. 1-6). IEEE.
  • Santana-Castolo, M. H., Morales, J. A., Torres-Ramos, S., & Alanis, A. Y. (2015, March). Study of particle swarm optimization algorithms using message passing interface and graphical processing units employing a high performance computing cluster. In International Conference on Supercomputing in Mexico (pp. 116-131). Springer, Cham.
  • Quintal, G., Sanchez, E. N., Alanis, A. Y., & Arana-Daniel, N. G. (2015, May). Real-time FPGA decentralized inverse optimal neural control for a shrimp robot. In 2015 10th System of Systems Engineering Conference (SoSE) (pp. 250-255). IEEE.
  • Romero-Aragon, J. C., Sanchez, E. N., & Alanis, A. Y. (2014, December). Glucose level regulation for diabetes mellitus type 1 patients using FPGA neural inverse optimal control. In 2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) (pp. 1-7). IEEE.
  • Lopez, V. G., Sanchez, E. N., & Alanis, A. Y. (2014). Real-time implementation of a neural inverse optimal control for a linear induction motor. In Advance Trends in Soft Computing (pp. 163-170). Springer, Cham.
  • Romero-Aragon, J. C., Sanchez, E. N., & Alanis, A. Y. (2014, August). Fpga neural identifier for insulin-glucose dynamics. In 2014 World Automation Congress (WAC) (pp. 675-680). IEEE.
  • Ornelas-Tellez, F., Graff, M., Sanchez, E. N., & Alanis, A. Y. (2014). PSO optimal tracking control for state-dependent coefficient nonlinear systems. In Advance Trends in Soft Computing (pp. 403-410). Springer, Cham.
  • Arana-Daniel, N., Villaseñor, C., López-Franco, C., & Alanís, A. Y. (2014, November). Bio-inspired aging model-particle swarm optimization and geometric algebra for structure from motion. In Iberoamerican Congress on Pattern Recognition (pp. 762-769). Springer, Cham.
  • Lastire, E. A., Sanchez, E. N., Alanis, A. Y., & Ornelas-Tellez, F. (2014). Passivity Analysis of Discrete Inverse Optimal Control Based on Control Lyapunov Functions CLF. IFAC Proceedings Volumes, 47(3), 6971-6975.
  • López-Franco, C., Gomez-Avila, J., Arana-Daniel, N., & Alanis, A. Y. (2014, August). Robot pose estimation based on visual information and particle swarm optimization. In 2014 World Automation Congress (WAC) (pp. 768-773). IEEE.
  • López-Franco, C., López-Franco, M., Sanchez, E. N., & Alanis, A. Y. (2014, August). Neural control of a mobile robot with monocular visual feedback. In 2014 World Automation Congress (WAC) (pp. 561-566). IEEE.
  • Arana-Daniel, N., Gallegos, A. A., López-Franco, C., & Alanis, A. Y. (2014, July). Smooth global and local path planning for mobile robot using particle swarm optimization, radial basis functions, splines and Bézier curves. In 2014 IEEE congress on evolutionary computation (CEC) (pp. 175-182). IEEE.
  • López-Franco, C., López-Franco, M., Sanchez, E. N., & Alanis, A. Y. (2014, July). Intelligent visual servoing for nonholonomic mobile robots. In 2014 International Joint Conference on Neural Networks (IJCNN) (pp. 1488-1493). IEEE.
  • Rangel, E., Alanís, A. Y., Ricalde, L. J., Arana-Daniel, N., & López-Franco, C. (2014, November). Bio-inspired aging model particle swarm optimization neural network training for solar radiation forecasting. In Iberoamerican Congress on Pattern Recognition (pp. 682-689). Springer, Cham.
  • López-Franco, C., Hernández-Barragán, J., López-Franco, M., Arana-Daniel, N., & Alanís, A. Y. (2014, November). Plane Detection Using Particle Swarm Optimization and Conformal Geometric Algebra. In Iberoamerican Congress on Pattern Recognition (pp. 852-859). Springer, Cham.
  • López-Franco, M., Sanchez, E. N., Alanis, A. Y., López-Franco, C., & Arana-Daniel, N. (2014, August). Discrete-time decentralized inverse optimal neural control combined with sliding mode for mobile robots. In 2014 World Automation Congress (WAC) (pp. 496-501). IEEE.
  • Alanis, A. Y., Arana-Daniel, N., & Lopez-Franco, C. (2013, August). Neural-PSO second order sliding mode controller for unknown discrete-time nonlinear systems. In The 2013 International Joint Conference on Neural Networks (IJCNN) (pp. 1-6). IEEE.
  • Lopez, V. G., Sanchez, E. N., & Alanis, A. Y. (2013, August). Neural inverse optimal control for a linear induction motor. In The 2013 International Joint Conference on Neural Networks (IJCNN) (pp. 1-6). IEEE.
  • Guevara-Reyes, E., Alanis, A. Y., Arana-Daniel, N., & Lopez-Franco, C. (2013, November). Integration of an inverse optimal control system with reinforced-SLAM for path planning and mapping in dynamic environments. In 2013 IEEE International Autumn Meeting on Power Electronics and Computing (ROPEC) (pp. 1-6). IEEE.
  • López-Franco, M., Sanchez, E. N., Alanis, A. Y., & Arana-Daniel, N. (2013, August). Real-time decentralized inverse optimal neural control for a Shrimp robot. In The 2013 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE.
  • López-Franco, M., Sanchez, E. N., Alanis, A. Y., & López-Franco, C. (2013, August). Discrete time neural control of a nonholonomic mobile robot integrating stereo vision feedback. In The 2013 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
  • Rios, J. D., Alanis, A. Y., Rivera, J., & Hernandez-Gonzalez, M. (2013, August). Real-time discrete neural identifier for a linear induction motor using a dSPACE DS1104 board. In The 2013 international joint conference on neural networks (IJCNN) (pp. 1-6). IEEE.
  • Leon, B. S., Alanis, A. Y., Sanchez, E. N., Ornelas-Tellez, F., & Ruiz-Velazquez, E. (2013, August). Subcutaneous neural inverse optimal control for an artificial pancreas. In The 2013 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
  • Lopez-Franco, M., Sanchez, E. N., Alanis, A. Y., & Arana-Daniel, N. (2013, June). Discrete-time decentralized inverse optimal neural control for a shrimp robot. In 2013 American Control Conference (pp. 1183-1188). IEEE.
  • Leon, B. S., Alanis, A. Y., Sanchez, E. N., Ornelas-Tellez, F., & Ruiz-Velazquez, E. (2013). Neural inverse optimal control applied to type 1 diabetes mellitus patients. Analog Integrated Circuits and Signal Processing, 76(3), 343-352.
  • Lopez, V. G., Sanchez, E. N., & Alanis, A. Y. (2013, June). PSO neural inverse optimal control for a linear induction motor. In 2013 IEEE Congress on Evolutionary Computation (pp. 1976-1982). IEEE.
  • Alanis, A. Y., Arana-Daniel, N., Lopez-Franco, C., & Sanchez, E. N. (2013, June). PSO-gain selection to improve a discrete-time second order sliding mode controller. In 2013 IEEE Congress on Evolutionary Computation (pp. 971-975). IEEE.
  • Hernandez-Vargas, E. A., Alanis, A. Y., & Sanchez, E. N. (2012, June). Discrete-time neural observer for HIV infection dynamics. In World Automation Congress 2012 (pp. 1-6). IEEE.
  • Alanis, A. Y., Simetti, C., Ricalde, L. J., & Odone, F. (2012, June). A wind speed neural model with particle swarm optimization kalman learning. In World Automation Congress 2012 (pp. 1-5). IEEE.
  • Leon, B. S., Alanis, A. Y., Sanchez, E. N., Ornelas, F., & Ruiz-Velazquez, E. (2012, June). Subcutaneous blood glucose neural inverse optimal control for type 1 diabetes mellitus patients. In World Automation Congress 2012 (pp. 1-6). IEEE.
  • Ricalde, L. J., Cruz, B., Catzin, G., Alanis, A. Y., & Sanchez, E. N. (2012, June). Forecasting for smart grid applications with higher order neural networks. In World Automation Congress 2012 (pp. 1-6). IEEE.
  • Lastire, E. A., Alanis, A. Y., & Sanchez, E. N. (2012, September). Inverse optimal neural control with speed gradient for a power electric system with changes in loads. In 2012 9th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) (pp. 1-6). IEEE.
  • Loukianov, A., Rivera, J., Alanis, A. Y., & Raygoza, J. J. (2012, September). Super-twisting sensorless control of linear induction motors. In 2012 9th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) (pp. 1-5). IEEE.
  • Leon, B. S., Alanis, A. Y., Sanchez, E. N., Ornelas-Tellez, F., & Ruiz-Velazquez, E. (2013). Neural inverse optimal control applied to type 1 diabetes mellitus patients. Analog Integrated Circuits and Signal Processing, 76(3), 343-352.
  • Ruiz-Velázquez, E., Alanis, A. Y., Femat, R., & Quiroz, G. (2011, August). Neural modeling of the blood glucose level for type 1 diabetes mellitus patients. In 2011 IEEE International Conference on Automation Science and Engineering (pp. 696-701). IEEE.
  • Salome, A., Alanis, A. Y., & Sanchez, E. N. (2011, October). Discrete-time sliding mode controllers for nonholonomic mobile robots trajectory tracking problem. In 2011 8th International Conference on Electrical Engineering, Computing Science and Automatic Control (pp. 1-6). IEEE.
  • López-Franco, C., Fink, G., Arana-Daniel, N., & Alanis, A. Y. (2011, October). A visual servo control based on geometric algebra. In 2011 8th International Conference on Electrical Engineering, Computing Science and Automatic Control (pp. 1-6). IEEE.
  • Lopez-Franco, M., Salome-Baylón, A., Alanis, A. Y., & Arana-Daniel, N. (2011, October). Discrete super twisting control algorithm for the nonholonomic mobile robots tracking problem. In 2011 8th International Conference on Electrical Engineering, Computing Science and Automatic Control (pp. 1-5). IEEE.
  • Leon, B. S., Alanis, A. Y., Sanchez, E. N., Ornelas, F., & Ruiz-Velazquez, E. (2011, December). Inverse optimal trajectory tracking for discrete time nonlinear positive systems. In 2011 50th IEEE Conference on Decision and Control and European Control Conference (pp. 1048-1053). IEEE.
  • Alanis, A. Y., Sanchez, E. N., Ruiz-Velazquez, E., & Leon, B. S. (2011, July). Neural model of blood glucose level for Type 1 Diabetes Mellitus Patients. In The 2011 International Joint Conference on Neural Networks (pp. 2018-2023). IEEE.
  • Alanis, A. Y., Lopez-Franco, M., Arana-Daniel, N., & Lopez-Franco, C. (2011, July). Discrete-time neural identifier for electrically driven nonholonomic mobile robots. In The 2011 International Joint Conference on Neural Networks (pp. 1067-1073). IEEE.
  • Ricalde, L. J., Catzin, G. A., Alanis, A. Y., & Sanchez, E. N. (2011, April). Higher order wavelet neural networks with Kalman learning for wind speed forecasting. In 2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG) (pp. 1-6). IEEE.
  • Alanis, A. Y., Sanchez, E. N., Hernandez-Gonzalez, M., & Ricalde, L. J. (2011, April). Discrete-time reduced order neural observer for linear induction motors. In 2011 IEEE Symposium on Computational Intelligence Applications In Smart Grid (CIASG) (pp. 1-7). IEEE.
  • Alanis, A. Y., & Sanchez, E. N. (2010, September). Second order sliding mode for MIMO nonlinear uncertain systems based on a neural identifier. In 2010 World Automation Congress (pp. 1-6). IEEE.
  • Alanis, A. Y., Sanchez, E. N., Loukianov, A. G., & Perez-Cisneros, M. A. (2010, June). Discrete time nonlinear identification via recurrent high order neural networks for a three phase induction motor. In International Symposium on Neural Networks (pp. 719-726). Springer, Berlin, Heidelberg.
  • Alanis, A. Y., Sanchez, E. N., & Ricalde, L. J. (2010). Discrete-time reduced order neural observers for uncertain nonlinear systems. International journal of neural systems, 20(01), 29-38.
  • Alanis, A. Y., Sanchez, E. N., & Hernandez, E. A. (2009, January). Reduced neural observers for a class of MIMO discrete-time nonlinear system. In 2009 6th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) (pp. 1-6). IEEE.
  • Alanis, A. Y., Ricalde, L. J., & Sanchez, E. N. (2009, June). High order neural networks for wind speed time series prediction. In 2009 International Joint Conference on Neural Networks (pp. 76-80). IEEE.
  • Alanis, A. Y., Sanchez, E. N., & Loukianov, A. G. (2008). Discrete-time backstepping synchronous generator stabilization using a neural observer. IFAC Proceedings Volumes, 41(2), 15897-15902.
  • Alanis, A. Y., Sanchez, E. N., & Loukianov, A. G. (2008, September). Real-time output trajectory tracking using a discrete neural backstepping controller. In 2008 IEEE International Symposium on Intelligent Control (pp. 1289-1294). IEEE.
  • Alanis, A. Y., Sanchez, E. N., & Loukianov, A. G. (2008, June). Real-time discrete recurrent high order neural observer for induction motors. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) (pp. 1012-1018). IEEE.
  • Sanchez, E. N., Alanis, A. Y., & Loukianov, A. G. (2007, June). Discrete-time recurrent high order neural observer for induction motors. In International Fuzzy Systems Association World Congress (pp. 711-721). Springer, Berlin, Heidelberg.
  • Alanis, A. Y., Sanchez, E. N., & Loukianov, A. G. (2007, December). Discrete-time backstepping induction motor control using a sensorless recurrent neural observer. In 2007 46th IEEE Conference on Decision and Control (pp. 6112-6117). IEEE.
  • Alanis, A. Y., Sanchez, E. N., & Loukianov, A. G. (2007, August). Discrete-time backstepping neural control for synchronous generators. In 2007 International Joint Conference on Neural Networks (pp. 2569-2574). IEEE.
  • Alanis, A. Y., Sanchez, E. N., & Loukianov, A. G. (2007, August). Discrete-time backstepping neural control for synchronous generators. In 2007 International Joint Conference on Neural Networks (pp. 2569-2574). IEEE.
  • Alanis, A. Y., Sanchez, E. N., & Loukianov, A. G. (2007, October). Discrete-time output trajectory tracking for induction motor using a neural observer. In 2007 IEEE 22nd International Symposium on Intelligent Control (pp. 584-589). IEEE.
  • Alanis, A. Y., Sanchez, E. N., & Loukianov, A. G. (2006, October). Discrete-time nonlinear recurrent high order neural observer. In 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control (pp. 1620-1624). IEEE.
  • Alanis, A. Y., Sanchez, E. N., & Loukianov, A. G. (2006, July). Discrete-time recurrent neural induction motor control using Kalman learning. In The 2006 IEEE International Joint Conference on Neural Network Proceedings (pp. 1993-2000). IEEE.
  • Alanis, A. Y., Sanchez, E. N., Loukianov, A. G., & Chen, G. (2006, December). Discrete-time output trajectory tracking by recurrent high-order neural network control. In Proceedings of the 45th IEEE Conference on Decision and Control (pp. 6367-6372). IEEE.
  • Sanchez, E. N., Alanis, A. Y., & Rico, J. (2004, July). Electric load demand prediction using neural network trained by Kalman filtering. In 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541) (Vol. 4, pp. 2771-2775). IEEE.
 
DOCENCIA
  • PhD in Electronic and Computer Engineering, CUCEI, UdG: Intelligent Control 2014-to date.
  • Master in Sciences in Electric Engineering, CUCEI, UdG: Artificial Intelligence 2012-to date.
  • Master in Sciences in Electronic and Computer Engineering, CUCEI, UdG: Artificial Neural Networks 2008-to date. Adaptive Control 2008-to date. Advanced Mathematics (Optimization), 2008-to date
  • Computer Science Engineering, CUCEI, UdG: Artificial Neural Networks , 2008- to date. Evolutive Algorithms, 2016- to date.
  • PhD in Sciences, CUCienega, UdG: Microsystems Theory I, 2014-to date.
  • PhD in Water and Energy, CUTonala, UdG: Systems Identification, 2012.
  • Master in Sciences in Water and Energy, CUTonala, UdG: Systems Identification, 2012.
  • Master in Automatic Control s, Technologic University of Tepic (UTT), México. Digital Control, 2007.
 
TESIS DIRIGIDAS
  • Eduardo Rangel Carrillo, PhD Program on Electronic and Computational Scieces, UdG, Thesis title: Algoritmo de Optimización por Enjambre de Partículas con Modelo de Envejecimiento Inspirado Biológicamente.
  • Jacob Morales González Muro, PhD Program on Electronic and Computational Scieces, UdG, Thesis title: Avances en descriptores de objetos.
  • Michel Emanuel Lopez Franco, PhD Program on Electrical Engineering, Center of Research and Advanced Studies, Campus Guadalajara (CINVESTAV), Tesis titled: Contról neuronal óptimo inverso para pacientes con diabetes mellitus.tipo1, Graduated on December 2013.
  • Blanca Selenia Leon Rodriguez, PhD Program on Electrical Engineering, Center of Research and Advanced Studies, Campus Guadalajara (CINVESTAV), Tesis titled: Contról neuronal óptimo inverso para pacientes con diabetes mellitus.tipo1, Graduated on December 2013.
  • Jorge Carlos Romero Aragón México, MS program, Center of Research and Advanced Studies, Campus Guadalajara (CINVESTAV), Theses Title: 2014 Implementación en FPGA de un control neuronal óptimo inverso para la regulación de nivel de glucosa en un paciente con diabetes mellitus tipo 1, Graduated on November 2014.
  • Enrique Alan Lastire Olmedo, PhD Program on Electrical Engineering, Center of Research and Advanced Studies, Campus Guadalajara (CINVESTAV), January 2012-present.
  • Michel Emanuel Lopez Franco, PhD Program, Center of Research and Advanced Studies, Campus Guadalajara (CINVESTAV), January 2013- present.
  • Eduardo Rangel Carrillo, PhD Program on Computer and Electronics Engineering, CUCEI, UdG, August 2013-present.
  • Jorge Rios Arrañaga, MS program, CUCEI, UdG, Thesis title: Control de un motor de inducción lineal, Graduated on December 2013.
  • Edgar Alberto Guevara, MS program, Thesis title: Control and path planning integration for a mobile robot, Graduated on November 2013.
  • Eduardo Rangel Carrillo, MS program, CUCEI, Udg, Thesis title: Entrenamiento de redes neuronales artificiales utilizando optimización por enjambre de partículas, Graduated on February 2013.
  • Michel Emanuel Lopez Franco, MS Program, CUCEI, UdG, Thesis title: Inverse Optimal Neural Control for a differential Robot, CUCEI, UdG, Graduated on December 2011.
  • Victor Gabriel Lopez Mejia, MS program, Center of Research and Advanced Studies, Campus Guadalajara (CINVESTAV), Theses Title: Control neuronal optimo inverso para un motor de inducción lineal, Graduated on November 2013.
  • Enrique Alan Lastire Olmedo, MS program, Center of Research and Advanced Studies, Campus Guadalajara (CINVESTAV), Theses Title: Control neuronal óptimo inverso para un sistema multimáquina de potencia, Graduated on December 2012.
  • Angel Salome Baylon, MS program, Center of Research and Advanced Studies, Campus Guadalajara (CINVESTAV), Theses Title: Controladores por modos deslizantes en tiempo discrete aplicados a robots móviles no holonomicos, Graduated on December 2011.
  • Daniel Landa Orta, MS Program on Computer and Electronics Engineering, CUCEI, UdG, 2012-present.
  • Manuel Humberto Santana Castolo, MS Program on Computer and Electronics Engineering, CUCEI, UdG, Theses Title: Implementación de biblioteca de algoritmos bio-inspirados en paralelo para el cluster Agave, December 2015.
  • Carlos EmilioTorres Cerna, MS Program on Computer and Electronics Engineering, CUCEI, UdG, Theses Title: Algoritmos de optimización bioinspirados y sus aplicaciones en biología sistémica, December 2015.
  • Maria Isabel Cibrian Decena, MS Program on Computer and Electronics Engineering, CUCEI, UdG, Theses Title: Reconocimiento de formas humanas en poses no pedestres por medio de descriptores localmente ponderados, December 2015.
 
PROYECTOS DE INVESTIGACIÓN
  • Discrete-time Neural Control using Kalman Filtering, CONACYT Mexico, March 2008-February 2009.
  • Recurrent Neural Networks to Modeling Nonlinear Systems, PROMEP Mexico, February 2010-February 2015.
  • High Order Neural Control Via block Control and Inverse Optimal Control, CONACYT Mexico, February 2010-January 2013.
  • System biology approach to personalize treatment in influenza virus infection (OPTREAT), CONACYT-DAAD (Mexico-Germany), 2014-2016.
  • Control Neuronal de Alto Orden Discreto para Sistemas No Lineales Inciertos con Retardos Desconocidos, February 2010-January 2013.
 
PREMIOS Y DISTINCIONES
  • Member of the Mexican Academy of Sciences.
  • Winner of the 2015 Fellowship “Catedra Marcos Moshinsky” UNAM-CONACYT.
  • Senior Member IEEE (Jun 2014).
  • Member of the National Researchers System (México), Level 2.
  • Winner of the 2013 L'Oréal- UNESCO- AMC International Fellowship (Mexico).
  • Associated Editor for the Journal of the Franklin Institute (Elsevier) 2010-to date.
  • Associated Editor for the Journal: Intelligent Automation & Soft Computing (Taylor and Francis) 2014-to date.
  • Subject Editor for the Journal of the Franklin Institute (Elsevier) 2015-to date.
  • Invited Editor for the Journal: Intelligent Automation & Soft Computing (Taylor & Francis) 2014.
  • Board member of the Mexican Association on Automatic Control (IFAC National Member Organization) 2010-2014.
  • IEEE recognition 2013 for her contribution to the field of science and its contribution to the development of the scientific literature.
  • General Co-chair of the “2012 World Automation Congress”.
  • General Chair of the first spring school of AMCA in Smart grids, may 2011.
  • Keynote speaker, World Automation Congress 2012, Puerto Vallarta, México.
  • Keynote speaker, of the “Foro de egresados del Instituto Tecnológico de Durango”, México 2014.
  • Keynote speaker of the “Semana de Ingeniería 2011”, Universidad Autónoma del Carmen.
  • Invited speaker at the “Seminario de Computación del Centro de Investigación en Matemáticas A. C.”, Guanajuato, México, 2014.
  • Invited speaker at the “Seminario de Investigación de la División de Matemáticas Aplicadas del Instituto Potosino de Investigación Científica y Tecnológica”, San Luis Potosí, México, 2011.
  • Member of the Program Comite of the “International Symposium on Neural Networks 2010”, Shangai, China, 2010, 2011, 2012.
  • Member of the Program Comite of the “International Workshop on Advanced Computational Intelligence 2010”, Shangai, China, 2010, 2011, 2012, 2013.
  • Session Chair on the “World Automation Congress 2010”, Kobe, Japan.
  • Organizing Committee of the 2011 spring course on smart grids, AMCA 2011.
  • Organizing Committee Co-Chair, “World Automation Congress 2012”, Puerto Vallarta Mexico.
  • Session Chair on the “Congress of the Mexican Association on Automatic Control”.
  • Member of the Mexican Association on Automatic Control.
  • Reviewer of Research Projects for CONACYT Mexico.
  • Best student of the generation, Electrical Engineering, Electric and Electronics Department, Technological Institute of Durango, Durango Mexico, 1997- 2002.
  • Nomination to the best student paper award in the IEEE ISIC’06, Munich, Germany, 2006.