Discrete-Time Neural Observers ：Analysis and Applications

Publication subTitle ：Analysis and Applications

Author： Alanis   Alma Y.;Sanchez   Edgar N  

Publisher： Elsevier Science‎

Publication year： 2017

E-ISBN: 9780128105443

P-ISBN(Paperback): 9780128105436

Subject： TP3 Computers

Keyword： 生物科学,化学,临床医学

Language： ENG

Access to resources Favorite

Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.

Description

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) algorithm
  • Contains fu

Chapter

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 Definitions

2.2 Introduction to Artificial 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

3 Full Order Neural Observers

3.1 Linear Output Case

3.2 Nonlinear Output Case

3.3 Applications

3.3.1 Human Immunodeficiency 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 Identifiers

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

5.2 Time-Delay Nonlinear System

5.3 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

The users who browse this book also browse


No browse record.