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Differential neural networks for robust nonlinear control : identification, state estimation and trajectory tracking /

This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a...

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Bibliographic Details
Main Authors: Poznyak, Alexander S
Corporate Authors: World Scientific Firm
Group Author: Sanchez, Edgar N; Yu, Wen, profesor titular
Published: World Scientific Pub. Co.,
Publisher Address: Singapore :
Publication Dates: 2001.
Literature type: eBook
Language: English
Subjects:
Neural networks Computer science
Nonlinear control theory
Robust control
Electronic books
Online Access: http://www.worldscientific.com/worldscibooks/10.1142/4703#t=toc
Summary: This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its applicat
Carrier Form: 1 online resource (xxxi,422pages) : illustrations
Bibliography: Includes bibliographical references and index.
ISBN: 9789812811295
CLC: TP273
Contents: 1. Neural networks structures. 1.1. Introduction. 1.2. Biological .eural networks. 1.3. Neuron model. 1.4. Neural networks structures. 1.5. Neural networks in control. 1.6. Conclusions. 1.7. References -- 2. Nonlinear system identification: Differential learning. 2.1. Introduction. 2.2. Identification error stability analysis for simplest differential neural networks without hidden layers. 2.3. Multilayer differential neural networks for nonlinear system on-line identification. 2.4. Illustrating examples. 2.5. Conclusion. 2.6. References -- 3. Sliding mode identification: Algebraic learning.

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