Dynamic Neural Networks: Structures and Training Methods

Abstract

Abstract Chapter 2 discusses the neural network approach to modeling and control of dynamical systems. The classes of ANN models for dynamical systems and their structural organization are considered in this chapter, including static networks (feedforward networks) and dynamic networks (recurrent networks). The next significant problem that arises in the formation of ANN models of dynamical systems is related to the algorithms of their learning, so also algorithms for learning dynamic ANN models are considered. The difficulties associated with such learning, as well as ways to overcome them, are analyzed. One of the fundamental requirements for the considered ANN models is the property of adaptability. Methods for satisfying this requirement are considered, including the use of ANN models with interneurons and subnets of interneurons, as well as the incremental formation of ANN models. One of the critical problems when generating ANN models, especially dynamical system models, is an acquisition of training sets. In this chapter, the specific features of processes needed to generate training sets for the ANN modeling of dynamical systems are analyzed. We consider direct and indirect approaches to the generation of these training sets. Algorithms for generating a set of test maneuvers and test excitation signals for the dynamical system required to obtain a representative set of training data are given.