Distributed representation of fuzzy rules and its application to pattern classification
Fuzzy Sets and Systems
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Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Similarity measures in fuzzy rule base simplification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On generating FC3 fuzzy rule systems from data usingevolution strategies
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An effective neuro-fuzzy paradigm for machinery condition healthmonitoring
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Semantic constraints for membership function optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
A proposal for improving the accuracy of linguistic modeling
IEEE Transactions on Fuzzy Systems
Compact and transparent fuzzy models and classifiers through iterative complexity reduction
IEEE Transactions on Fuzzy Systems
Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base
IEEE Transactions on Fuzzy Systems
Q learning based on self-organizing fuzzy radial basis function network
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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Using optimization tools such as genetic algorithms to construct a fuzzy expert system (FES) focusing only on its accuracy without considering the comprehensibility may not result in a system that produces understandable expressions. To exploit the transparency characteristics of FES for reasoning in a 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 for a FES to determine the degree of the accuracy, the comprehensibility of the fuzzy sets, and the completeness of fuzzy rule structure. These quantitative measures are then used as a fitness function for a genetic algorithm in optimally refining a FES.