Discrete-Time Neural Observers: Analysis and Applications - Softcover

Alanis, Alma Y.

Softcover

ISBN 10: 0128105437 ISBN 13: 9780128105436

Publisher: Academic Press, 2017

Synopsis

Discrete-Time Neural Observers: Analysis and Applications presents recent advances in the theory of neural state estimation for discrete-time unknown nonlinear systems with multiple inputs and outputs. The book includes rigorous mathematical analyses, based on the Lyapunov approach, that guarantee their properties. In addition, for each chapter, simulation results are included to verify the successful performance of the corresponding proposed schemes. In order to complete the treatment of these schemes, the authors also present simulation and experimental results related to their application in meaningful areas, such as electric three phase induction motors and anaerobic process, which show the applicability of such designs. The proposed schemes can be employed for different applications beyond those presented. The book presents solutions for the state estimation problem of unknown nonlinear systems based on two schemes. For the first one, a full state estimation problem is considered; the second one considers the reduced order case with, and without, the presence of unknown delays. Both schemes are developed in discrete-time using recurrent high order neural networks in order to design the neural observers, and the online training of the respective neural networks is performed by Kalman Filtering. Presents online learning for Recurrent High Order Neural Networks (RHONN) using the Extended Kalman Filter (EKF) algorithmContains full and reduced order neural observers for discrete-time unknown nonlinear systems, with and without delaysIncludes rigorous analyses of the proposed schemes, including the nonlinear system, the respective observer, and the Kalman filter learningCovers real-time implementation and simulation results for all the proposed schemes to meaningful applications

About this title

Review:

Presents advances in the theory of neural state estimation for discrete-time unknown nonlinear systems with multiple inputs and outputs

About the Author:

Alma Y. Alanis, was born in Durango, Durango, Mexico, in 1980. She received her B. Sc degree from Instituto Tecnologico de Durango (ITD, Durango Campus, Durango, Durango) in 2002, and 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 Campus, Mexico) in 2004 and 2007 respectively. Since 2008 she has been with the University of Guadalajara, where she is currently a Chair Professor in the Department of Computer Science. She is also a member of the Mexican National Research System (SNI-2). She has published papers in recognized International Journals and Conferences, along with two international books. She is a Senior Member of the IEEE and Subject and Associated Editor of the Journal of Franklin Institute (Elsevier) and Intelligent Automation & Soft Computing (Taylor & Francis); moreover, she is currently serving on a number of IEEE and IFAC Conference Organizing Committees. In 2013 she received the grant for women in science by L'Oreal-UNESCO-AMC-CONACYT-CONALMEX. In 2015, she received the Marcos Moshinsky Research Award. Since 2008, she has been a member of the Accredited Assessors record (RCEA-CONACYT), evaluating a wide range of national research projects. She has belonged to important project evaluation committees for national and international research projects. Her research interests are in neural control, backstepping control, block control, and their applications to electrical machines, power systems and robotics.

About this edition

• Publisher: Academic Press
• Publication date: 2017
• ISBN 10: 0128105437
• ISBN 13: 9780128105436
• Binding: Paperback
• Number of pages: 150