Nonlinear system identification using neural networks

Abstract

In this Chapter we treat the problem of nonlinear system identification using neural networks. Model structures and their parametrization by multilayer perceptrons are discussed, together with learning algorithms, practical aspects and examples. The Chapter is organized as follows. In Section 3.1 we review model structures such as NARX, NARMAX and nonlinear state space models. In Section 3.2 parametrizations of these models by multilayer neural nets are made. Neural state space models are introduced. In Section 3.3 classical as well as advanced on- and off-line learning algorithms are presented and their relation with nonlinear optimization theory is explained in Section 3.4. Section 3.5 concerns practical aspects of model validation, model complexity and aspects of pruning and regularization. In Section 3.6 neural network models are interpreted as uncertain linear systems. Finally in Section 3.7 simulated and real life examples are presented on nonlinear system identification using feedforward as well as recurrent type of neural networks. New contributions are made in Sections 3.2.2, 3.2.3, 3.3.2, 3.6 and 3.7.