Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Sugeno type controllers are universal controllers
Fuzzy Sets and Systems
Fuzzy Systems as Universal Approximators
IEEE Transactions on Computers
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Necessary conditions for some typical fuzzy systems as universal approximators
Automatica (Journal of IFAC)
Fuzzy function approximation with ellipsoidal rules
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
Function approximation based on fuzzy rules extracted frompartitioned numerical data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A fuzzy classifier with ellipsoidal regions
IEEE Transactions on Fuzzy Systems
Reduction of fuzzy rule base via singular value decomposition
IEEE Transactions on Fuzzy Systems
Approximation theory of fuzzy systems-MIMO case
IEEE Transactions on Fuzzy Systems
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The structure of fuzzy models produced by a heursitic analysis of the problem domain is compared with that of models algorithmically generated from training data. The trade-offs between granularity, specificity, interpretability, and efficiency are examined for rule-bases produced in each of these manners. An algorithm that combines rule learning with region merging is introduced to incorporate beneficial features of both the heuristic and learning approaches to producing fuzzy models.