Interval valued fuzzy sets based on normal forms
Fuzzy Sets and Systems
Least squares model fitting to fuzzy vector data
Fuzzy Sets and Systems
Fuzzy data analysis by possibilistic linear models
Fuzzy Sets and Systems - Fuzzy Numbers
Quotients with respect to similarity relations
Fuzzy Sets and Systems - Mathematics and Fuzziness, Part 1
Evaluation of fuzzy linear regression models
Fuzzy Sets and Systems
Exponential possibility regression analysis
Fuzzy Sets and Systems - Special issue on fuzzy information processing
Constructing fuzzy models by product space clustering
Fuzzy model identification
Rapid prototyping of fuzzy models based on hierarchical clustering
Fuzzy model identification
Fuzzy functions and their fundamental properties
Fuzzy Sets and Systems
Type 2 representation and reasoning for CWW
Fuzzy Sets and Systems - Special issue: Approximate Reasoning in Words
A fuzzy analysis of country-size argument for the Feldstein-Horioka puzzle
Information Sciences: an International Journal
Evolution of Fuzzy System Models: An Overview and New Directions
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - FUZZYSS’2009
Decision making with imprecise parameters
International Journal of Approximate Reasoning
Information Sciences: an International Journal
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''Fuzzy Functions'' are proposed to be determined by the least squares estimation (LSE) technique for the development of fuzzy system models. These functions, ''Fuzzy Functions with LSE'' are proposed as alternate representation and reasoning schemas to the fuzzy rule base approaches. These ''Fuzzy Functions'' can be more easily obtained and implemented by those who are not familiar with an in-depth knowledge of fuzzy theory. Working knowledge of a fuzzy clustering algorithm such as FCM or its variations would be sufficient to obtain membership values of input vectors. The membership values together with scalar input variables are then used by the LSE technique to determine ''Fuzzy Functions'' for each cluster identified by FCM. These functions are different from ''Fuzzy Rule Base'' approaches as well as ''Fuzzy Regression'' approaches. Various transformations of the membership values are included as new variables in addition to original selected scalar input variables; and at times, a logistic transformation of non-scalar original selected input variables may also be included as a new variable. A comparison of ''Fuzzy Functions-LSE'' with Ordinary Least Squares Estimation (OLSE)'' approach show that ''Fuzzy Function-LSE'' provide better results in the order of 10% or better with respect to RMSE measure for both training and test cases of data sets.