Neural Network Modeling and Identification of Dynamical Systems
Authors: Yury Tiumentsev – Mikhail Egorchev
ISBN-10: 0128152540
ISBN-13: 9780128152546
Edition 版本: 1
Released: 2019-05-31
Print Length 页数: 332 pages
Book Description
Neural Network Modeling and Identification of Dynamical Systems presents a new approach on how to obtain the adaptive neural network models for complex systems that are typically found in real-world applications. The book introduces the theoretical knowledge available for the modeled system into the purely empirical black box model,thereby converting the model to the gray box category. This approach significantly reduces the dimension of the resulting model and the required size of the training set. This book offers solutions for identifying controlled dynamical systems,as well as identifying characteristics of such systems,in particular,the aerodynamic characteristics of aircraft.
Covers both types of dynamic neural networks (black box and gray box) including their structure,synthesis and training
Offers application examples of dynamic neural network technologies,primarily related to aircraft
Provides an overview of recent achievements and future needs in this area
Introduction
1The Modeling Problem for Controlled Motion of Nonlinear Dynamical Systems
2 Dynamic Neural Networks: Structures and Training Methods
3 Neural Network Black Box Approach to the Modeling and Control of Dynamical Systems
4Neural Network Black Box Modeling of Nonlinear Dynamical Systems: Aircraft Controlled
Motion
5 Semiempirical Neural Network Models of Controlled Dynamical Systems
6Neural Network Semiempirical Modeling of Aircraft Motion
APPENDIX A.Results of Computational Experiments With Adaptive Systems
Index
Back Cover
