We introduce a new category of fuzzy neural networks with multiple-output based on fuzzy clustering algorithm, especially, fuzzy c-means clustering algorithm (FCM-based FNNm) for pattern classification in this paper. The premise part of the rules of the proposed networks is realized with the aid of the scatter partition of input space generated by FCM clustering algorithm. The partitioned local...

Takagi-Sugeno (TS) fuzzy modeling and control techniques are applied to the classical nonlinear Ball and Beam problem. The nonlinear model is segmented to different local linear models. Local controllers are hence designed using LMI theory for achieving a robust control behavior for ball position. TS fuzzy modeling is applied to generate suitable fuzzy models, and the associated controllers. Us...

in this study, we introduce and study a concept of distributed fuzzymodeling. fuzzy modeling encountered so far is predominantly of a centralizednature by being focused on the use of a single data set. in contrast to this style ofmodeling, the proposed paradigm of distributed and collaborative modeling isconcerned with distributed models which are constructed in a highly collaborativefashion. i...

Fuzzy set theory has been extensively applied to extend various data models and resulted in numerous contributions, mainly with respect to the popular relational model or to some related form of it. To satisfy the need of modeling complex objects with imprecision and uncertainty, recently many researches have been concentrated on fuzzy semantic (conceptual) and object-oriented data models. This...

This paper outlines two contributions to stability analysis of fuzzy systems: knowledge of the membership function shape (actually, constraints on the membership function values) and polynomial approaches. Both ideas reduce the conservativeness in the stability analysis of a nonlinear system when expressed as a fuzzy model by using membership shape information and by allowing a more general cla...

In this paper, we present coreset constructions for the fuzzy Kmeans problem. First, we show that one can construct a weak coresets for fuzzy K-means. Second, we show that there are coresets for fuzzy K-means with respect to balanced fuzzy K-means solutions. Third, we use these coresets to develop a randomized approximation algorithm whose runtime is polynomial in the number of the given points...

Fuzzy linear regression models can provide an estimated fuzzy number that has a fuzzy membership function. If a point that has the highest membership value from the estimated fuzzy number is not within the support of the observed fuzzy membership function, a decisionmaker can have high risk from the estimate. In this study a new distance, between fuzzy numbers is proposed. On the basis of this ...

introduction the ardabil plain aquifer, with area about 900 km2, has high concentration amounts of nitrate in some parts. nowadays, nitrate pollution in groundwater due to the widespread application of fertilizers and increasing of drinking water demand, has encountered consumers with problem. the adverse health effects of high nitrate levels in drinking water have been well documented. in the ...

1 An approach to fuzzy control of nonlinear systems: Stability and design issues Wang, HO; Tanaka, K; Griffin, MF IEEE TRANSACTIONS ON FUZZY SYSTEMS 1029 STABILITY ANALYSIS AND DESIGN OF FUZZY CONTROL-SYSTEMS TANAKA, K; SUGENO, M FUZZY SETS AND SYSTEMS 1013 Fuzzy regulators and fuzzy observers: Relaxed stability conditions and LMI-based designs Tanaka, K; Ikeda, T; Wang, HO IEEE TRANSACTIONS ON...