Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Fuzzy Modeling for Control
Rule-based modeling: precision and transparency
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A hybrid approach to modeling metabolic systems using a geneticalgorithm and simplex method
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Similarity measures in fuzzy rule base simplification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Data-driven linguistic modeling using relational fuzzy rules
IEEE Transactions on Fuzzy Systems
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The modeling and identification of uncertain and nonlinear systems are important but challenging problems. Because of numerous advantages fuzzy models are often preferred to describe such systems. However, in many cases the generated models are very complex. Therefore, in this paper a combined method of fuzzy rules extraction, besides their simplification and optimization for creating a compact fuzzy rule base of Takagi-Sugeno (TS) models, is proposed that can be effectively used to represent complex systems. The initial fuzzy rule base is generated by using our proposed simple method of fuzzy rule extraction. The extracted rules are simplified using set-theory based simplification method and thereafter, simplified rules are further optimized using genetic algorithm. This, in turn, improves the accuracy of the model that was degraded during simplification. The novelty lies in the approach of fuzzy rule extraction method. The results are compared with those obtained by standard linear, neuro-fuzzy and advanced fuzzy clustering based identification tools.