A fuzzy LMS neural network method for evaluation of importance of indices in MADM

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

A fuzzy LMS (least-mean-square algorithm) neural network evaluation model, with fuzzy triangular numbers as inputs, is set up to compare the importance of different indices. The model can determine attribute or index weights (importance) automatically so that they are more objectively and accurately distributed. The model also has a strong self-learning ability so that calculations are greatly reduced and simplified. Further, decision maker's specific preferences for uncertainty, i.e., risk-averse, risk-loving or risk-neutral, are considered in the evaluation model. Hence, our method can give objective results while taking into decision maker's subjective intensions. Meanwhile, it is simple. A numerical example is given to illustrate the method.