Quantitative measures of the accuracy, comprehensibility, and completeness of a fuzzy expert system

Using optimization tools such as genetic algorithms to construct a fuzzy expert system (FES), focusing only on its accuracy without considering comprehensibility may result in a system that is not easy to understand or the so called a black box model. To exploit the transparency features of FESs for explanation in higher-level knowledge representation, a FES should provide high comprehensibility while preserving 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 degree of the accuracy, comprehensibility, and completeness of FESs. These quantitative measures are then used as a fitness function for a genetic algorithm in an optimally built FES