Discrete-Time Recurrent Neural Control: Analysis and Applications

Discrete-Time Recurrent Neural Control: Analysis and Applications 

The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The simulation results that appear in each chapter include rigorous mathematical analyses, based on the Lyapunov approach, to establish its properties. The book contains two sections: the first focuses on the analyses of control techniques; the second is dedicated to illustrating results of real-time applications. It also provides solutions for the output trajectory tracking problem of unknown nonlinear systems based on sliding modes and inverse optimal control scheme.

“This book on Discrete-time Recurrent Neural Control is unique in the literature, with new knowledge and information about the new technique of recurrent neural control especially for discrete-time systems.
The book is well organized and clearly presented. It will be welcome by a wide range of researchers in science and engineering, especially graduate students and junior researchers who want to learn the new notion of recurrent neural control. I believe it will have a good market.
It is an excellent book after all.”
― Guanrong Chen, City University of Hong Kong

“This book includes very relevant topics, about neural control. In these days, Artificial Neural Networks have been recovering their relevance and well-stablished importance, this due to its great capacity to process big amounts of data. Artificial Neural Networks development always is related to technological advancements; therefore, it is not a surprise that now we are being witnesses of this new era in Artificial Neural Networks, however most of the developments in this research area only focuses on applicability of the proposed schemes. However, Edgar N. Sanchez author of this book does not lose focus and include both important applications as well as a deep theoretical analysis of Artificial Neural Networks to control discrete-time nonlinear systems. It is important to remark that first, the considered Artificial Neural Networks are development in discrete-time this simplify its implementation in real-time; secondly, the proposed applications ranging from modelling of unknown discrete-time on linear systems to control electrical machines with an emphasize to renewable energy systems. However, its applications are not limited to these kind of systems, due to their theoretical foundation it can be applicable to a large class of nonlinear systems. All of these is supported by the solid research done by the author.”
― Alma Y. Alanis, University of Guadalajara, Mexico




This website strictly complies with DMCA Digital Copyright Laws.. Please be clear that we (edownloads.me) do not own copyrights of these e-books. The intention behind sharing these books and educational material is to provide easy access to students, researchers and other readers who don't have access to these books at their local libraries, "thus only for educational purpose". We highly encourage our readers to purchase this content from the respected publishers. If anyone holding copyrights wants us to remove this content, please contact us rightaway. All books and educational material on edownloads.me are free and NOT HOSTED ON OUR WEBSITE. If you feel that your copyrights have been violated, then please contact us immediately. You may send an email to infomanzastore@gmail.com for all DMCA / Removal Requests. edownloads.me doesn’t have any material hosted on the server of this page, only links to books that are taken from other sites on the web are published and these links are unrelated to the book server. edownloads.me server doesnot store any type of book or material. No illegal copies are made or any copyright © and / or copyright is damaged or infringed since all material is free on the internet.

Add a Comment