The synergy between classical and soft-computing techniques for time series prediction

Abstract

A new method for extracting valuable process information from input-output data is presented in this paper using a pseudo-gaussian basis function neural network with regression weights. The proposed methodology produces dynamical radial basis function, able to modify the number of neuron within the hidden layer. Other important characteristic of the proposed neural system is that the activation of the hidden neurons is normalized, which, as described in the bibliography, provides better performance than non-normalization. The effectiveness of the method is illustrated through the development of dynamical models for a very well known benchmark, the synthetic time series Mackey-Glass.