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Neuro-fuzzy classifying system for intelligent decision support. Part I. Methodology

机译：用于智能决策支持的神经模糊分类系统。第一部分：方法论

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摘要

The description of complex decision making processes is usually based on the combination of two types of knowledge and data: a qualitative, fuzzy one which contains elements of uncertainty and vagueness and often is expressed in the form of linguistic rules usually provided by a domain expert, and a quantitative, non-fuzzy one which appears in the form of measurements and other numerical data. This paper presents a methodology for the design of decision support systems. This methodology can effectively learn, represent, process and generalize both qualitative and quantitative knowledge and data contributing to the description of complex decision making processes. The proposed approach combines artificial neural networks with the theory of fuzzy sets giving a structure that can be called a neuro-fuzzy classifier. Part I of this paper presents this classifier in both learning and approximate-inference phases. Two decision support systems designed with the use of the proposed neuro-fuzzy classifiers are presented in Part II of this paper.
机译：复杂决策过程的描述通常基于两种类型的知识和数据的组合：定性，模糊的包含不确定性和模糊性的元素，通常以领域专家提供的语言规则的形式表示;以及一种量化的，无模糊的形式，以度量和其他数值数据的形式出现。本文提出了一种决策支持系统的设计方法。这种方法可以有效地学习，表示，处理和归纳定性和定量知识以及有助于描述复杂决策过程的数据。所提出的方法将人工神经网络与模糊集理论相结合，给出了一种可以称为神经模糊分类器的结构。本文的第一部分在学习和近似推理阶段都介绍了该分类器。本文的第二部分介绍了使用建议的神经模糊分类器设计的两个决策支持系统。

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