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Discrete-time neural observersanalys...

Alanis, Alma Y.,

Discrete-time neural observersanalysis and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Discrete-time neural observersAlma Y. Alanis, Edgar N. Sanchez.
Reminder of title:	analysis and applications /
Author:	Alanis, Alma Y.,
other author:	Sanchez, Edgar N.,
Published:	London :Academic Press, an imprint of Elsevier,2017.
Description:	1 online resource :ill.
Subject:	Discrete-time systems.
Online resource:	https://www.sciencedirect.com/science/book/9780128105436
ISBN:	9780128105443 (electronic bk.)

Discrete-time neural observersanalysis and applications /
Alanis, Alma Y.,

Discrete-time neural observersanalysis and applications /[electronic resource] :Alma Y. Alanis, Edgar N. Sanchez. - London :Academic Press, an imprint of Elsevier,2017. - 1 online resource :ill.

Includes bibliographical references adn index.

Front Cover; Discrete-Time Neural Observers; Copyright; Contents; About the Authors; Acknowledgment; 1 Introduction; 1.1 Introduction; 1.2 Motivation; 1.3 Objectives; 1.4 Problem Statement; 1.5 Book Structure; 1.6 Notation; References; 2 Mathematical Preliminaries; 2.1 Stability De nitions; 2.2 Introduction to Arti cial Neural Networks; 2.2.1 The Neuron; 2.2.2 Feedforward Neural Networks; 2.2.3 Recurrent Neural Networks; 2.3 Discrete-Time High Order Neural Networks; 2.4 The EKF Training Algorithm; 2.5 Introduction to Nonlinear Observers; 2.5.1 Observer Problem Statement; References.

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.

ISBN: 9780128105443 (electronic bk.)Subjects--Topical Terms:

182123
Discrete-time systems.
Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: QA402

Dewey Class. No.: 003.75

Discrete-time neural observersanalysis and applications /
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100 1 $a Alanis, Alma Y., $e author. $3 867218
245 1 0 $a Discrete-time neural observers $h [electronic resource] : $b analysis and applications / $c Alma Y. Alanis, Edgar N. Sanchez.
260 $a London : $b Academic Press, an imprint of Elsevier, $c 2017.
300 $a 1 online resource : $b ill.
504 $a Includes bibliographical references adn index.
505 0 $a Front Cover; Discrete-Time Neural Observers; Copyright; Contents; About the Authors; Acknowledgment; 1 Introduction; 1.1 Introduction; 1.2 Motivation; 1.3 Objectives; 1.4 Problem Statement; 1.5 Book Structure; 1.6 Notation; References; 2 Mathematical Preliminaries; 2.1 Stability De nitions; 2.2 Introduction to Arti cial Neural Networks; 2.2.1 The Neuron; 2.2.2 Feedforward Neural Networks; 2.2.3 Recurrent Neural Networks; 2.3 Discrete-Time High Order Neural Networks; 2.4 The EKF Training Algorithm; 2.5 Introduction to Nonlinear Observers; 2.5.1 Observer Problem Statement; References.
505 8 $a 3 Full Order Neural Observers3.1 Linear Output Case; 3.2 Nonlinear Output Case; 3.3 Applications; 3.3.1 Human Immunode ciency Virus (HIV); 3.3.2 Rotatory Induction Motor; 3.3.3 Linear Induction Motor; 3.3.4 Anaerobic Digestion; References; 4 Reduced Order Neural Observers; 4.1 Reduced Order Observers; 4.2 Neural Identi ers; 4.3 Linear Output Case; 4.4 Nonlinear Output Case; 4.5 Applications; 4.5.1 van der Pol System; 4.5.2 RONO for the HIV Model; 4.5.3 Rotatory Induction Motor; 4.5.4 Linear Induction Motor; References; 5 Neural Observers with Unknown Time-Delays; 5.1 Introduction.
505 8 $a 5.2 Time-Delay Nonlinear System5.3 Full Order Neural Observers for Unknown Nonlinear Systems with Delays; 5.3.1 Extended Kalman Filter Training Algorithm; 5.4 Reduced Order Neural Observers for Unknown Nonlinear Systems with Delays; 5.5 Applications; 5.5.1 van der Pol System; 5.5.2 Linear Induction Motor; References; 6 Final Remarks; 6.1 Final Remarks; Index; Back Cover.
520 $a 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.
588 0 $a Online resource; title from PDF title page (EBSCO, viewed February 16, 2017).
650 0 $a Discrete-time systems. $3 182123
650 0 $a Nonlinear control theory. $3 182079
650 0 $a Neural networks (Computer science) $3 181982
655 4 $a Electronic books. $2 local. $3 214472
700 1 $a Sanchez, Edgar N., $e author. $3 867219
856 4 0 $u https://www.sciencedirect.com/science/book/9780128105436