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- W2187549228 abstract "This paper deals with the linear fuzzy regression model with fuzzy data and crisp non-negative parameters. To find the best values of the param- eters of the fuzzy model, we minimize the sum of squared errors of lower and upper level functions of fuzzy error function. Regression analysis is one of the most applicable tool used in methods of esti- mation. It deals with the investigation of the dependence of a variable upon one or more variables. The aim of regression analysis is to estimate the parameters those occur in its particular mathematical form. Usually, method of least square is applied to determine the values of parameters. The variables involve in the regression model are not always crisp in nature. To make more realistic regression model, we fuzzify the variables. Instead of dealing with exact data points, we can consider the approximate data points which repre- sents in terms of fuzzy numbers. In this case, we get the fuzzy regression model which can be further classified into linear or non-linear form. Fuzzy regression analysis is introduced by Tanaka in (14). His approach was based on linear programming. After that, many authors (2, 6, 7, 8, 11, 12, 15) have studied this theory using different approaches. Both variables and parameters are fuzzy in the model discussed by (2, 12). In this paper, we consider the following linear fuzzy regression model with fuzzy data points and crisp parameters." @default.
- W2187549228 created "2016-06-24" @default.
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- W2187549228 date "2015-01-01" @default.
- W2187549228 modified "2023-09-26" @default.
- W2187549228 title "LINEAR FUZZY REGRESSION MODEL" @default.
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