Neural Network Modeling and Identification of Dynamical Systems

Neural Network Modeling and Identification of 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 available for the modeled system 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, in particular, the aerodynamic characteristics of aircraft. The book also offers a use of dynamic neural networks to solve problems of adaptive control of complex systems. Covers both types of dynamic neural networks (black box and gray box) including their structure, synthesis and trainingOffers application examples of dynamic neural network technologies, primarily related to aircraftProvides an overview of recent achievements and future needs in this area INDICE: 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

• ISBN: 978-0-12-815254-6
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 220
• Fecha Publicación: 01/06/2019
• Nº Volúmenes: 1
• Idioma: Inglés