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.
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Table of Contents
1. The modeling problem for controlled motion of nonlinear dynamical systems 2. Neural network approach to the modeling and control of dynamical systems 3. Neural network black box (empirical) modeling of nonlinear dynamical systems for the example of aircraft controlled motion 4. Neural network semi-empirical models of controlled dynamical systems 5. Neural network semi-empirical modeling of aircraft motion
