In this paper, optimization of a linear objective function with fuzzy relational inequality constraints is investigated where the feasible region is formed as the intersection of two inequality fuzzy systems and Dombi family of t-norms is considered as fuzzy composition. Dombi family of t-norms includes a parametric family of continuous strict t-norms, whose members are increasing functions of ...

A robust method, fuzzy kNNModel, for toxicity prediction of chemical compounds is proposed. The method is based on a supervised clustering method, called kNNModel, which employs fuzzy partitioning instead of crisp partitioning to group clusters. The merits of fuzzy kNNModel are two-fold: (1) it overcomes the problems of choosing the parameter ε allowed error rate in a cluster, and the parameter...

Pattern recognition methods can be divided into four di erent categories: statistical or probabilistic, structural, possibilistic or fuzzy, and neural methods. A formal analysis shows that there is a computational complexity versus representational power trade-o between probabilistic and possibilistic or fuzzy set measures, in general. Furthermore, sigmoidal theory shows that fuzzy set membersh...

Artificial recharge can be an effective method to raise the groundwater table and to resolve the groundwater crisis in Sefid dasht plain. The most important step to successful accomplishment of artificial recharge is locating suitable areas for artificial recharge. Hence this research carried out with purpose of determining suitable areas for artificial recharge in Sefid dasht plain. Slope, sur...

K e y w o r d s C o m p e n s a t o r y , Fuzzy similarity measure, Inverted wedge system, Backpropagation algorithm. 1. I N T R O D U C T I O N Recently, the neural fuzzy approach to system modeling has become a popular research topic [110]. Moreover, the neural fuzzy method possesses the advantages of both the pure neural and the fuzzy methods; it brings the low-level learning and computation...

The practical realization of the Fuzzy Logic Controler (FLC) usually depends on the application. Using the parameter-depended group of fuzzy operators like distance based operators in the approximate reasoning process the FLC components can be adapted to achieve better results. The program language environment, applied by simulation, supports the choosing of suitable fuzzy operators and their p...

This paper transforms fuzzy number into clear number using the centroid method, thus we can research the traditional linear regression model which is transformed from the fuzzy linear regression model. The model’s input and output are fuzzy numbers, and the regression coefficients are clear numbers. This paper considers the parameter estimation and impact analysis based on data deletion. Throug...

This paper deals with ridge estimation of fuzzy nonparametric regression models using triangular fuzzy numbers. This estimation method is obtained by implementing ridge regression learning algorithm in the Lagrangian dual space. The distance measure for fuzzy numbers that suggested by Diamond is used and the local linear smoothing technique with the crossvalidation procedure for selecting the o...

This paper proposes a fuzzy abductive inference with degrees of manifestation. The fuzzy logic is applied to Peng and Reggia's abductive inference for handling the manifestation degrees. This method infers irredundant combinations of candidates with degrees of belief for the manifestations. Learning algorithm for updating the fuzzy causations and t-conorm parameter is also presented in this pap...

This paper considers robust mean-variance portfolio selection problems including uncertainty sets and fuzzy factors. Since these problems are not well-defined problems due to fuzzy factors, it is hard to solve them directly. Therefore, introducing chance constraints, fuzzy goals and possibility measures, the proposed models are transformed into the deterministic equivalent problems. Furthermore...