A committee machine with two-layer expert nets
Purpose - The purpose of this paper is to present a committee machine (CM) with two-layer expert nets to overcome the lack of approximating ability of CM with single-layer expert nets. Design/methodology/approach - A frequently used structure of CM, with a fuzzy c-means clustering algorithm as splitting and combining unit and some single-layer linear neural nets as expert modules, was applied to short-term climate prediction. Considering the complexity of the climate conditions, use was made of two-layer back propagation (BP) neural nets instead of single-layer linear nets to test the effect of the model. Experiments were performed on both synthetic and realistic climatic data. Findings - Prediction accuracy is raised when the BP nets were used and as the number of hidden neurons increased at some stages. It implies that improving the approximating ability of individual expert module of a CM is beneficial. Research limitations/implications - The optimal learning rate, the optimal cluster numbers and the maximal number of iteration were not well treated. Practical implications - The paper is a useful alternative worth consideration for the complicated prediction problems. Originality/value - A CM with two-layer expert nets are presented. Comparisons are made between CMs with simple and complex expert nets.
neural nets cybernetics fuzzy logic