A NEW STRUCTURE FOR NONLINEAR SYSTEM IDENTIFICATION USING NEURAL NETWORKS

Abstract

Most industrial systems are nonlinear. In these applications the conventional identification and control techniques are effectively used if the nonlinearity of the system is known. When the system contains unknown nonlinearities, however. the conventional techniques exhibit poor performance. To tackle this problem a neural network is proposed to use. The ability of neural networks to approximate nonlinear relationships makes them prime candidate for applications in nonlinear system identification. Simulation results show that if the conventional nonlinear system description is used the modelling error may be significant, but using the delta transformation this error can be reduced. This paper demonstrates the difference between the shift and delta model and verifies the effectiveness of the structure of the delta transformation. Simulation results demonstrate this difference.