Nonlinear dynamic system identification using artificial neural networks (ANNs)

Abstract

A recurrent multilayer perceptron (MLP) network topology is used in the identification of nonlinear dynamic systems from only the input/output measurements. This effort is part of a research program devoted to developing real-time diagnostics and predictive control techniques for large-scale complex nonlinear dynamic systems. The identification is performed in the discrete-time domain, with the learning algorithm being a modified form of the back-propagation (BP) rule. The recurrent dynamic network (RDN) developed is used for the identification of a simple power plant boiler with known nonlinear behavior. Results indicate that the RDN can reproduce the nonlinear response of the boiler while keeping the number of nodes roughly equal to the relative order of the system. A number of issues are identified regarding the behavior of the RDN which are unresolved and require further research. Use of the recurrent MLP structure with a variety of different learning algorithms may prove useful in utilizing artificial neural networks for recognition, classification, and prediction of dynamic patterns