the purpose of this study is to investigate the accuracy of predictions of aniline removal efficiency in a moving bed biofilm reactor (mbbr) by various methods, namely by rbf, anfis, and fuzzy regression analysis. the reactor was operated in an aerobic batch and was filled by light expanded clay aggregate (leca) as a carrier for the treatment of aniline synthetic wastewater. exploratory data an...

kim and bishu (fuzzy sets and systems 100 (1998) 343-352) proposeda modification of fuzzy linear regression analysis. their modificationis based on a criterion of minimizing the difference of the fuzzy membershipvalues between the observed and estimated fuzzy numbers. we show that theirmethod often does not find acceptable fuzzy linear regression coefficients andto overcome this shortcoming, pr...

Since Tanaka et al. (1982) proposed a study of linear regression analysis with a fuzzy model, fuzzy regression analysis has been widely studied and applied in a variety of substantive areas. Regression analysis in the case of heterogeneity of observations is commonly presented in practice. The authors' main goal is to apply fuzzy clustering techniques to fuzzy regression analysis. Fuzzy cluster...

Where fuzzy regression can be applied and, in which conditions fuzzy regression method more appropriate tool for the investigations are identified in this paper. The contrast between fuzzy regression and ordinary regression analysis and three approach of fuzzy regression are summarized. In this paper, we estimated the values of the parameters in the factorial experiment in the textile industry ...

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 la- grangian dual space. the distance measure for fuzzy numbers that suggested by diamond is used and the local linear smoothing technique with the cross- validation procedure for selecting t...

When it is question of prediction, no deterministic model can be totally efficient, especially when the output to be estimated is dependent imprecisely on many fluctuant variables measuring human behavior (cognitions, choices, consumption, etc.). Regressions based on fuzzy logic which combine statistics and expert’s attitudes can be used to improve the estimation of such outputs. Those regressi...