Revised GMDH-type Neural Network Algorithm with a Feedback Loop Identifying Sigmoid Function Neural Network

Abstract

In this paper, a revised Group Method of Data Handling (GMDH)-type neural network algorithm with a feedback loop identifying sigmoid function neural network is proposed. In this algorithm, the optimum sigmoid function neural network architecture is automatically organized so as to minimize the prediction error criterion defined as Akaike's Information Criterion (AIC) or Prediction Sum of Squares (PSS) by using the heuristic self-organization. The structural parameters such as the number of neurons in each layer, the number of feedback loops and the useful input variables are automatically determined using AIC or PSS criterion. Therefore, it is easy to apply this algorithm to the identification problem of the complex nonlinear system and to obtain a good prediction results.