Fuzzy linear regression models with least square errors

To estimate the parameters of fuzzy linear regression models with fuzzy output and crisp inputs, we develop a mathematical programming model in this paper. The method is constructed on the basis of minimizing the square of the total difference between observed and estimated spread values or in other...

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Veröffentlicht in:Applied mathematics and computation Jg. 163; H. 2; S. 977 - 989
Hauptverfasser: Modarres, M., Nasrabadi, E., Nasrabadi, M.M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY Elsevier Inc 15.04.2005
Elsevier
Schlagworte:
Calculus of variations and optimal control
Exact sciences and technology
Fuzzy linear regression
Fuzzy numbers
Linear inference, regression
Mathematical analysis
Mathematical programming
Mathematics
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in mathematical programming
Numerical methods in mathematical programming, optimization and calculus of variations
Probability and statistics
Sciences and techniques of general use
Statistics
Fuzzy numbers
Fuzzy linear regression
Mathematical programming
Error estimation
Applied mathematics
Least squares method
Linear regression
Optimization method
Fuzzy number
Mathematical model
ISSN:0096-3003, 1873-5649
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Zusammenfassung:To estimate the parameters of fuzzy linear regression models with fuzzy output and crisp inputs, we develop a mathematical programming model in this paper. The method is constructed on the basis of minimizing the square of the total difference between observed and estimated spread values or in other words minimizing the least square errors. The advantage of the proposed approach is its simplicity in programming and computation as well as its performance. To compare the performance of the proposed approach with the other methods, two examples are presented.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2004.05.004