Neural Network Modeling and Identification of Dynamical Systems

A cutting-edge approach to model size in dynamic neural networks

Yury Tiumentsev (Author), Mikhail Egorchev (Author)

9780128152546

Paperback, published 17 May 2019

332 pages
23.4 x 19 x 2.2 cm, 0.63 kg

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.

1. The modeling problem for controlled motion of nonlinear dynamical systems2. Neural network approach to the modeling and control of dynamical systems3. Neural network black box (empirical) modeling of nonlinear dynamical systems for the example of aircraft controlled motion4. Neural network semi-empirical models of controlled dynamical systems5. Neural network semi-empirical modeling of aircraft motion

Subject Areas: Biomedical engineering [MQW]