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.
Author(s): Yury Tiumentsev Mikhail Egorchev
Edition: 1
Publisher: Academic Press
Year: 2019
Language: English
Pages: 324
Chapter 1: The modeling problem for controlled motion of nonlinear dynamical systems
1.1 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 systems
2.1 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 motion
3.1 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 systems
4.1 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 motion
5.1 Semi-empirical modeling of longitudinal short-period motion for a maneuverable aircraft
5.2 Identification of aerodynamic characteristics for a maneuverable aircraft
Conclusion
