Revised GMDH-type neural network algorithm self-selecting optimum neural network architecture

Abstract

In this study, the revised group method of data handling (GMDH)-type neural network (NN) algorithm self-selecting the optimum neural network architecture is applied to the identification of a nonlinear system. In this algorithm, the optimum neural network architecture is automatically organized using two kinds of neuron architecture, such as the polynomial- and sigmoid function-type neurons. Many combinations of the input variables, in which the high order effects of the input variables are contained, are generated using the polynomial-type neurons, and useful combinations are selected using the prediction sum of squares (PSS) criterion. These calculations are iterated, and the multilayered architecture is organized. Furthermore, the structural parameters, such as the number of layers, the number of neurons in the hidden layers, and the useful input variables, are automatically selected in order to minimize the prediction error criterion defined as PSS.