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Abstract:

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.

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Neuro-fuzzy classifying system for intelligent decision support. Part II. Applications