Neural Network Modeling and Identification of Dynamical Systems - Softcover

Tiumentsev, Yury; Egorchev, Mikhail

Softcover

ISBN 10: 0128152540 ISBN 13: 9780128152546

Publisher: Academic Press, 2019

Synopsis

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

About this title

About the Authors

Dr. Yury V. Tiumentsev is currently a full professor at Moscow Aviation Institute, teaching in subjects including computer science, computer-aided design, artificial intelligence, artificial neural networks, and soft computing. He is also the Vice President of the Russian Neural Network Society and Vice-Chairman of the Organization and Program Committee of the Annual All-Russia Scientific and Engineering Conference on Neuroinformatics. Dr. Tiumentsev is also a member of the Scientific Committee and a publication reviewer for the International Conference of Artificial Intelligence and Soft Computing (ICAISC), as well as other conference collections such as the International Joint Conference on Neural Networks (IJCNN). His current research subjects include artificial neural networks, adaptive systems, intelligent control, mathematical modeling and computer simulation of complex systems. Dr. Tiumentsev is the author of the Russian-language monograph entitled Neural Network Modeling of Aircraft Motion, and has also written more than 130 articles on his areas of expertise.

Mikahil Egorchev is currently a Senior R&D Software Engineer at RoboCV. He is presently working on his Ph.D. in Mathematical Modeling, Numerical Methods and Software Complexes at the Moscow Aviation Institute. He has published 13 articles in his subject areas, which include artificial neural networks, mathematical modeling and computer simulation of nonlinear dynamical systems, numerical optimization methods, and optimal control.

From the Back Cover

Neural Network Modeling and Identification of Controlled Dynamical Systems presents a new approach to obtain adaptive neural network models for complex systems typically found in real-world applications. The main idea behind this new method is to introduce theoretical knowledge of available modeled systems into the initial, 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 which includes aerospace aspects of aircraft systems. The book also offers a use of dynamic neural networks to solve problems of adaptive control of complex systems.

Edition details

• Publisher: Academic Press
• Publication date: 2019
• Language: English
• ISBN 10: 0128152540
• ISBN 13: 9780128152546
• Binding: Paperback
• Edition number: 1
• Number of pages: 332