A methodology to model fuzzy systems using fuzzy clustering in a rapid-prototyping approach

In this paper we present different approaches to the problem of fuzzy rules extraction by using fuzzy clustering. They try to investigate the use of fuzzy clustering as a technique that let us obtain a first approximation of the fuzzy model in a rapid-prototyping approach. The key ideas here are to generate in a first step a collection of fuzzy rules from numerical data pairs. Subsequently, these rules can be converted either in a collection of linguistic rules in order to obtain a descriptive approach, or in an inference machine so that we obtain an approximative approach. This can be considered as a quasi-descriptive approach, because it assigns a weight to the fuzzy rules (like in the descriptive approaches) but also it generates the fuzzy sets directly in the product space of the input variables (like in approximative approaches) both by using the information disclosed by means of a fuzzy clustering process.

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