Prediction and control of discrete systems using neural networks

Abstract

This paper discusses the prediction and control of nonlinear discrete systems using neural networks. The discrete systems discussed are neural networks which could be either radial basis functions (RBF) or cerebellar model articulation controller (CMAC). The stability features are guaranteed, i.e. the errors between the predicted values and the actual values in prediction or the errors between the desired values and the actual values in control are bounded. Theoretical results are strict and examples are employed to explain the theoretical results.