Overview
Strengthens understanding of neural networks for readers working on control theory, including various mathematical proofs and analyses;
Closely examines the use of neural networks for the control of uncertain dynamical systems;
Facilitates implementation of adaptive structures using updating rules originating in optimization algorithms;
Presents system identification, state estimation, and control schemes, applicable to a wide range of systems.
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Table of contents (6 chapters)
Keywords
About this book
Authors and Affiliations
About the authors
Kasra Esfandiari is a PhD candidate at The Center for Systems Science, Yale University, New Haven, CT, United States.
Farzaneh Abdollahi is Associate Professor at the Department of Electrical Engineering, AmirKabir University, Tehran, Iran and Adjunct Assistant Prof. at Dept. of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada.
Heidar Ali Talebi is Professor at the Department of Electrical Engineering, AmirKabir University, Tehran, Iran and Adjunct Professor at the Department of Electrical Engineering, University of Western Ontario, London, ON, Canada.
Bibliographic Information
Book Title: Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems
Authors: Kasra Esfandiari, Farzaneh Abdollahi, Heidar A. Talebi
DOI: https://doi.org/10.1007/978-3-030-73136-6
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-030-73135-9Published: 19 June 2021
Softcover ISBN: 978-3-030-73138-0Published: 20 June 2022
eBook ISBN: 978-3-030-73136-6Published: 18 June 2021
Edition Number: 1
Number of Pages: XXIII, 163
Number of Illustrations: 2 b/w illustrations, 76 illustrations in colour
Topics: Complexity, Artificial Intelligence, Mathematical Models of Cognitive Processes and Neural Networks, Power Electronics, Electrical Machines and Networks