Constructing membership functions using statistical data
Fuzzy Sets and Systems
Membership function as an evaluation
Fuzzy Sets and Systems
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to artificial neural systems
Introduction to artificial neural systems
Fuzzy regression analysis using neural networks
Fuzzy Sets and Systems
Fuzzy regression by fuzzy number neural networks
Fuzzy Sets and Systems
Fuzzy regression using asymmetric fuzzy coefficients and fuzzified neural networks
Fuzzy Sets and Systems
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm
IEEE Transactions on Neural Networks
Generalized beta prior models on fraction defective in reliability test planning
Journal of Computational and Applied Mathematics
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In a regression analysis, it is assumed that the observations come from a single class in a data cluster and the simple functional relationship between the dependent and independent variables can be expressed using the general model; Y=f(X)+@e. However; a data cluster may consist of a combination of observations that have different distributions that are derived from different clusters. When faced with issues of estimating a regression model for fuzzy inputs that have been derived from different distributions, this regression model has been termed the 'switching regression model' and it is expressed with Y^L=f^L(X)+@e^L(L=@?"i"="1^pl"i). Here l"i indicates the class number of each independent variable and p is indicative of the number of independent variables [J.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transaction on Systems, Man and Cybernetics 23 (3) (1993) 665-685; M. Michel, Fuzzy clustering and switching regression models using ambiguity and distance rejects, Fuzzy Sets and Systems 122 (2001) 363-399; E.Q. Richard, A new approach to estimating switching regressions, Journal of the American Statistical Association 67 (338) (1972) 306-310]. In this study, adaptive networks have been used to construct a model that has been formed by gathering obtained models. There are methods that suggest the class numbers of independent variables heuristically. Alternatively, in defining the optimal class number of independent variables, the use of suggested validity criterion for fuzzy clustering has been aimed. In the case that independent variables have an exponential distribution, an algorithm has been suggested for defining the unknown parameter of the switching regression model and for obtaining the estimated values after obtaining an optimal membership function, which is suitable for exponential distribution.