Neural Networks Modeling and Control Applications for Unknown Nonlinear Delayed Systems in Discrete Time
Auteurs : Rios Jorge D., Y Alanis Alma, Arana-Daniel Nancy, Lopez-Franco Carlos
Directeur de Collection : Sanchez Edgar N.
Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time focuses on modeling and control of discrete-time unknown nonlinear delayed systems under uncertainties based on Artificial Neural Networks. First, a Recurrent High Order Neural Network (RHONN) is used to identify discrete-time unknown nonlinear delayed systems under uncertainties, then a RHONN is used to design neural observers for the same class of systems. Therefore, both neural models are used to synthesize controllers for trajectory tracking based on two methodologies: sliding mode control and Inverse Optimal Neural Control.
As well as considering the different neural control models and complications that are associated with them, this book also analyzes potential applications, prototypes and future trends.
1. Introduction2. Mathematical preliminaries3. Recurrent high order neural network identification of nonlinear discrete-time unknown system with time-delays4. Neural identifier-control scheme for nonlinear discrete-time unknown system with time-delays5. Recurrent high order neural network observer of nonlinear discrete-time unknown systems with time-delays6. Neural observer-control scheme for nonlinear discrete-time unknown system with time-delays7. Concluding remarks and future trends
AppendixA. Artificial neural networksB. Linear induction motor prototypeC. Differential robot prototype
Dr. Alma Y. Alanis received her M.Sc. and Ph.D. degrees in electrical engineering from the Advanced Studies and Research Center of the National Polytechnic Institute (CINVESTAV-IPN), Guadalajara, Mexico. Since 2008 she has been with University of Guadalajara, where she is currently a Dean of the Technologies for Cyber-Human Interaction Division, CUCEI. She is also member of the Mexican National Research System (SNI-2) and member of the Mexican Academy of Sciences. She has published papers in recognized International Journals and Conferences, besides eight international books. Dr. Alanis is a Senior Member of the IEEE and Subject Editor of the Journal of Franklin Institute, Section Editor at Open Franklin, Technical Editor at ASME/IEEE Transactions on Mechatronics, and Associate Editor at IEEE Transactions on Cybernetics, Intelligent Automation & Soft Computing and Engeenering Applications of Artifical Intelligence. Moreover, Dr. Alanis is currently serving on a number of IEEE and IFAC Conference Organizing Committees. In 2013 Dr. Alanis received the grant for women in science by L'Oreal-UNESCO-AMC-CONACYT-CONALMEX. In 2015, she received the Marcos Moshinsky Research Award. Her research interest centers on artificial neural networks, learning systems, intelligent control, and intelligent systems.
Nancy Arana-Daniel received her B. Sc. Degree from the University of Guadalajara in 2000, and her M. Sc. And Ph.D. degrees in electric engineering with the special field in computer sicence from Research Center o
- Provide in-depth analysis of neural control models and methodologies
- Presents a comprehensive review of common problems in real-life neural network systems
- Includes an analysis of potential applications, prototypes and future trends
Date de parution : 01-2020
Ouvrage de 158 p.
19x23.3 cm