Reduction of neural network models for identification and control of nonlinear systems

Structural learning is a proven pruning technique which induces decay of redundant weights. This paper introduces a method to significantly reduce the size of multilayer feedforward neural networks used as plant models and controllers. Initially oversized models are reduced during training thereby eliminating the need for a priori model order selection. A modification of structural learning is used to train the networks. Several examples nonlinear identification and control are presented. Order reduction can be performed both off-line and online. The reduced neural models and controllers lessen the computational load and thus benefit real time applications.