Fuzzy RBF neural network model for multiple attribute decision making

Authors:
Feng Kong;Hongyan Liu
Affiliations:
School of Business Administration, North China Electric Power University, Baoding, P.R. China;School of Business Administration, North China Electric Power University, Baoding, P.R. China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

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Abstract

This paper studies how to compare and select one best alternative, from the new alternatives, according to historical or current ones. Previous methods not only need a lot of data but also are complex. So, we put forward an RBF neural network method that not only has the advantages of common neural network methods, but also need much less samples and are straightforward. The number of neurons at the hidden level is easily determined. This model can determine attribute weights automatically so that weights are more objectively and accurately distributed. Further, decision maker's specific preferences for uncertainty, i.e., risk-averse, risk-loving or risk-neutral, are considered in the determination of weights. Hence, our method can give objective results while taking into decision maker's subjective intensions. A numerical example is given to illustrate the method.