Quantitative Measures of a Fuzzy Expert System
نویسنده
چکیده
Abstract: Using optimization tools such as genetic algorithms (GAs) to construct a fuzzy expert system (FES) focusing only on its accuracy without considering the comprehensibility may result in a system that is not easy to understand. To exploit the transparency features of FESs for explanation in higher-level knowledge representation, a FES should provide high comprehensibility while preserves its accuracy. The completeness of fuzzy sets and rule structures should also be considered to guarantee that every data point has a response output. This paper proposes some quantitative measures to determine the degrees of the accuracy, the comprehensibility, and the completeness of FESs. These quantitative measures are then used as a fitness function for a genetic algorithm in optimally constructing a FES.
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