MCFS: a multiple criteria reasoning fuzzy expert systems building tool

A. Kamel, A. Nazif, O. El-Dessouki, N. Kamel
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引用次数: 2

Abstract

The authors present the design principles of MCFS, an expert system building tool based on the idea of combining multiple criteria reasoning with the concepts of fuzzy logic. An important feature of MCFS is its ability to handle multiple criteria reasoning by structuring the deduction process into a hierarchy of logical levels. Within each level, the rules are organized into sets of rules and rule set groups. The concepts of fuzzy logic are introduced by allowing uncertainty within each rule, each set of rules, and each rule set group. MCFS has been fully implemented and applied to several domains of knowledge such as computer system selection and procurement, solution of nonlinear simultaneous equations, neck-tie selection, and longevity estimation. Experiments with these applications indicate that, compared to standard expert system tools, MCFS produces expert systems which are easier to build and better match the human expert.<>
多准则推理模糊专家系统构建工具
本文介绍了基于多准则推理与模糊逻辑概念相结合的专家系统构建工具MCFS的设计原理。MCFS的一个重要特征是它能够通过将演绎过程构建成逻辑层次结构来处理多标准推理。在每个级别中,规则被组织成规则集和规则集组。模糊逻辑的概念是通过允许每个规则、每个规则集和每个规则集组中的不确定性来引入的。MCFS已经完全实现并应用于几个知识领域,如计算机系统选择和采购,非线性联立方程的求解,领带选择和寿命估计。这些应用程序的实验表明,与标准专家系统工具相比,MCFS产生的专家系统更容易构建,并且与人类专家更匹配。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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