Neural Network Modeling and Identification of Dynamical Systems
- 1st Edition - May 17, 2019
- Latest edition
- Authors: Yury Tiumentsev, Mikhail Egorchev
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 1 5 2 5 4 - 6
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 1 5 4 3 0 - 4
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… Read more
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
Researchers, engineers, and post-doc students in neural engineering, computational engineering, and mathematical modeling
Introduction
Chapter 1
: The modeling problem for controlled motion of nonlinear dynamical systems1.1 The dynamical system as an object of study
1.2 Dynamical systems and the problem of adaptability
1.3 Classes of problems arising from the processes of development and operation for dynamical systems
1.4 A general approach to solve the problem of DS modeling
Chapter 2:
Neural network approach to the modeling and control of dynamical systems2.1 Classes of ANN models for dynamical systems and their structural organization
2.2 Acquisition problem for training sets needed to implement ANN models for dynamical systems
2.3 Algorithms for learning ANN models
2.4 Adaptability of ANN models
Chapter 3:
Neural network black box (empirical) modeling of nonlinear dynamical systems for the example of aircraft controlled motion3.1 Neural network empirical DS models
3.2 ANN model of motion for aircrafts based on a multilayer neural network
3.3 Performance evaluation for ANN models of aircraft motion based on multilayer neural networks
3.4 The use of empirical-type ANN models for solving problems of adaptive fault-tolerant control of nonlinear dynamical systems operating under uncertain conditions
Chapter 4:
Neural network semi-empirical models of controlled dynamical systems4.1 The relationship between empirical and semi-empirical ANN models for controlled dynamical systems
4.2 The model-building process for semi-empirical ANN models
4.3 A preparation example for the semi-empirical ANN model of a simple dynamical system
4.4 An experimental evaluation of semi-empirical ANN model capabilities
Chapter 5:
Neural network semi-empirical modeling of aircraft motion5.1 Semi-empirical modeling of longitudinal short-period motion for a maneuverable aircraft
5.2 Identification of aerodynamic characteristics for a maneuverable aircraft
Conclusion
References
- Edition: 1
- Latest edition
- Published: May 17, 2019
- Language: English
YT
Yury Tiumentsev
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.
Affiliations and expertise
Full Professor, Computer-Aided Design Department, Department of Flight Dynamics and Control, Numerical Mathematics and Computer Programming Department, Moscow Aviation Institute, RussiaME
Mikhail Egorchev
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.
Affiliations and expertise
Affiliation: Senior R&D Software Engineer, RoboCV LLC., RussiaRead Neural Network Modeling and Identification of Dynamical Systems on ScienceDirect