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

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