Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Extracting Interpretable Fuzzy Rules from RBF Networks
Neural Processing Letters
Soft computing in human centered systems thinking
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
On generating FC3 fuzzy rule systems from data usingevolution strategies
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Ideal fuzzy rules systems are supposed to be flexible, complete, consistent, compact and comprehensible (FC4). This paper describes the use of the interactive genetic algorithms (IGAs) to acquire FC4 fuzzy rules of complex system. The fuzzy sets of fuzzy rules are explained with the linguistic expressions through comparing with the standard linguistic variables. It is helpful to make the fuzzy rules to be comprehensible and conduct human evaluation during IGAs process. Not only quantitative evaluation but qualitative one is used to evaluate both the interpretability and control performance of the acquired fuzzy rules. The presented approach is applied to the control of the non-linear and coupled system with two control objectives. Simulation experiments show that the approach is feasible to acquire the FC4 fuzzy rules with good interpretabllity and good control performance.