Modelling dynamic processes with clustered time-delay neurons

This paper investigates the modelling capabilities of neural nets for a dynamic nonlinear process. Different neural structures are compared: multilayer perceptron (MLP) and radial basis function network (RBF) with an external tapped delay line, and modifications of both network types using internal delays, called time-delay MLP (TDMLP) and time-delay RBF (TDRBF). The nonlinear process to be modelled is a drive system including some nonlinearities, e.g. saturation effects. A special clustering procedure is introduced in order to increase the modelling accuracy, reduce computation and provide better generalisation.