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Differential Neural Networks Robust by Poznyak Alex (5 results)
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Differential Neural Networks for Robust Nonlinear Control
Published by World Scientific Publishing Comp, 2001
ISBN 10: 9810246242 ISBN 13: 9789810246242
Language: English
hardcover. Condition: Very Good. Fast Shipping - Safe and Secure 7 days a week!
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Differential Neural Networks for Robust Nonlinear Control
Published by World Scientific Publishing Company, 2001
ISBN 10: 9810246242 ISBN 13: 9789810246242
Language: English
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Differential Neural Networks for Robust Nonlinear Control: Identification, State Estimation and Trajectory Tracking
Published by WORLD SCIENTIFIC PUB CO INC, 2001
ISBN 10: 9810246242 ISBN 13: 9789810246242
Language: English
Condition: New. InhaltsverzeichnisTheoretical Study: Neural Networks Structures Nonlinear System Identification: Differential Learning Sliding Mode Identification: Algebraic Learning Neural State Estimation Passivation via Neuro Control Neuro Traje.
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Differential Neural Networks for Robust Nonlinear Control
Published by World Scientific Pub Co Inc, 2001
ISBN 10: 9810246242 ISBN 13: 9789810246242
Language: English
Hardcover. Condition: Like New. Like New. book.
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Differential Neural Networks for Robust Nonlinear Control: Identification, State Estimation and Trajectory Tracking
Published by World Scientific Publishing Company Okt 2001, 2001
ISBN 10: 9810246242 ISBN 13: 9789810246242
Language: English
Buch. Condition: Neu. Neuware - 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 application to various controlled physical systems (robotic, chaotic, chemical, etc.).
