论文信息 - Identification of the Multi-layered Neural Networks by Revised GMDH-Type Neural Network Algorithm with PSS Criterion

Identification of the Multi-layered Neural Networks by Revised GMDH-Type Neural Network Algorithm with PSS Criterion

In this paper, the revised GMDH-type neural network algorithm with PSS criterion for model selection is proposed. In this algorithm, the optimum multi-layered neural network architecture is automatically organized so as to minimize the prediction error criterion defined as PSS (Prediction Sum of Squares) by using the heuristic self-organization method. Both the sigmoid function type neural networks and the radial basis function type neural networks can be organized by this algorithm and the structural parameters such as the number of neurons in each layer, the number of layers and the useful input variables are automatically determined by using 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 accuracy.

Abhijit S. Pandya | Tadashi Kondo

[1] A. Ivakhnenko. Heuristic self-organization in problems of engineering cybernetics , 1970 .

[2] H. Akaike. A new look at the statistical model identification , 1974 .

[3] Katsuyuki Hagiwara,et al. On the problem of applying AIC to determine the structure of a layered feedforward neural network , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[4] H. Tamura,et al. Heuristics free group method of data handling algorithm of generating optimal partial polynomials with application to air pollution prediction , 1980 .

[5] Tadashi Kondo. Identification of radial basis function networks by using revised GMDH-type neural networks with a feedback loop , 2002, Proceedings of the 41st SICE Annual Conference. SICE 2002..