Multi-Attribute Decision Making using Competitive Neural Networks

M. Abdoos
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Abstract

Multi Attribute Decision Making (MADM) methods are widely used for making the optimal decision. Different approaches have been presented to solve decision-making problems. The aim of MADM is ranking of feasible alternatives. In this paper, a new approach to solve MADM problems using an artificial neural network has been presented. The competition among alternatives is modeled by a competitive network. The ordered list of the alternatives is achieved in two phases: partial ranking and fine ranking. The results of this approach are compared with Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS).
基于竞争神经网络的多属性决策
多属性决策(MADM)方法被广泛用于进行最优决策。人们提出了不同的方法来解决决策问题。MADM的目的是对可行的备选方案进行排序。本文提出了一种利用人工神经网络求解MADM问题的新方法。备选方案之间的竞争由一个竞争网络来模拟。可选方案的有序列表分为两个阶段:部分排序和精细排序。将该方法的结果与简单加性加权法(SAW)和理想解相似性排序偏好法(TOPSIS)进行了比较。
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