A GA-based fuzzy modeling approach for generating TSK models
Fuzzy Sets and Systems - Modeling and control
International Journal of Approximate Reasoning
Static output feedback controller design for fuzzy systems: An ILMI approach
Information Sciences: an International Journal
Data-driven fuzzy modeling for Takagi-Sugeno-Kang fuzzy system
Information Sciences: an International Journal
On constructing parsimonious type-2 fuzzy logic systems via influential rule selection
IEEE Transactions on Fuzzy Systems
A new approach to fuzzy wavelet system modeling
International Journal of Approximate Reasoning
Retail expansion decision based on improved marginal analysis
Expert Systems with Applications: An International Journal
A new method for fuzzy rule base reduction
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This paper develops a new approach to building Sugeno-type models. The essential idea is to separate the premise identification from the consequence identification, while these are mutually related in the previous methods. A fuzzy discretization technique is suggested to determine the premise of the model, and an orthogonal estimator is provided to identify the consequence of the model. The orthogonal estimator can provide information about the model structure, or which terms to include in the model, and final parameter estimates in a very simple and efficient manner. The well-known gas furnace data of Box and Jenkins is used to illustrate the proposed modeling approach and to compare its performance with other statistical and fuzzy modeling approaches. It shows that the performance of the new approach compares favorably with these existing techniques