Fuzzy modeling using genetic algorithms with fuzzy entropy as conciseness measure

Information Sciences

Volumn 136, Issue 1-4, 2001, Pages 53-67

  Suzuki, T ^a   Kodama, T ^b   Furuhashi, T ^b   Tsutsui, H ^c  

^a NAGOYA UNIVERSITY   (Japan)

^b MIE UNIVERSITY   (Japan)

^c YAMATAKE CORPORATION   (Japan)

Author keywords

Conciseness measure; Fuzzy entropy; Fuzzy modeling; Genetic algorithms; Linguistic interpretability

Indexed keywords

COMPUTATIONAL LINGUISTICS; GENETIC ALGORITHMS; MATHEMATICAL MODELS; MEMBERSHIP FUNCTIONS;

CONCISENESS MEASURES; FUZZY ENTROPY;

FUZZY SETS;

EID: 0035426528     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0020-0255(01)00141-4     Document Type: Article

References (17)

1. Fuzzy identification of systems and its applications to modeling and control
2. Rule-based modeling: Precision and transparency
3. Semantic constraints for membership function optimization
4. Ga-fuzzy modeling and classification: Complexity and performance
5. Selection of input variables using genetic algorithm for hierarchical fuzzy modeling
6. Information theory and an extension of the maximum likelihood principle
7. A learning method of fuzzy reasoning by delta rule
8. A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory
9. Measuring fuzzy uncertainty
10. On quantification of different facets of uncertainty
11. Image thresholding using fuzzy entropies
12. Fuzzy c-means as a regularization and maximum entropy approach
13. Multiple objective optimization with vector evaluated genetic algorithms
14. Pareto optimality, ga-easiness and deception
15. Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization
16. Fuzzy control under various approximate reasoning methods