A neural network approach to on-line identification of non-linear systems

Abstract

The authors introduce three aspects of the neural identification of nonlinear systems. First, a method of extending the error backpropagation neural network to enable it to perform online identification of a system is considered. This enables the investigation of adaptive nonlinear process control based on neural identification. Second, the neural identification has been successfully tested on a complex nonlinear composite system which includes formidable, but realistic, nonlinear process characteristics such as hysteresis. This has helped to demonstrate the general applicability of identification using neural techniques. Third, the novel method of neural identification was compared with online identification based on the well-established linear least-squares technique. The comparison highlights the faster adaptation of linear identification against the higher asymptotic accuracy of neural identification. >