一种混合模糊LMS神经网络模型用于MCDM中标准权重的确定

Feng Kong, Hongyan Liu
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引用次数: 0

摘要

建立了以模糊数为输入的混合模糊LMS神经网络模型,确定了各选择准则的权重。该模型可以根据市场的时间序列数据,自动确定各指标的权重,使其分布更加客观、准确。该模型还具有较强的自学习能力,使计算量大大减少和简化。此外,模型还考虑了决策者对不确定性的特定偏好。因此,该方法可以在考虑决策者对不确定性偏好的主观意图的情况下给出修正的结果。最后给出了数值算例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Hybrid Fuzzy LMS Neural Network Model for Determining Weights of Criteria in MCDM
A hybrid fuzzy LMS neural network model, with fuzzy numbers as inputs, is set up to determine the weights of each criterion of alternatives. The model can determine the weights of each criterion automatically, according to the time-series data of market, 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 also are considered in the model. Hence, this method can give revised results while taking into decision maker's subjective intensions for uncertainty preference. A numerical example is given to illustrate the method.
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