Fuzzy Multiple Objective Programming in Interval Piecewise Regression Model

Three shortcomings associated with the fuzzy regression model have been observed (Yu et al., 2005). First, in possibility analysis, Tanaka’s methodologies were extremely sensitive to outliers or high variabilities of data and they also ignored certain information contained in the data (Kim et al., 1...

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Vydáno v:Fuzzy Multiple Objective Decision Making s. 234 - 243
Hlavní autoři: Tzeng, Gwo-Hshiung, Huang, Jih-Jeng
Médium: Kapitola
Jazyk:angličtina
Vydáno: United Kingdom Chapman and Hall/CRC 2014
CRC Press LLC
Témata:
Applied mathematics
Automatic control engineering
Probability & statistics
Lee’s Method
Membership Functions
Multiple Objective Problem
Piecewise Regression
Fuzzy Linear Model
Data Set
Piecewise Regression Model
Fuzzy Linear Regression Model
LINGO Optimization Software
Multiple Objective Programming
Fuzzy Regression
Interval Output
Yu’s Models
Regression Model
Anti-ideal Solutions
Fuzzy Multiple Objective
Change Points
Fuzzy Regression Model
Possibility Model
2i Ij
Fuzzy Multiple Objective Programming
Necessity Models
QP
ISBN:1466554614, 9781466554610
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Popis
Shrnutí:Three shortcomings associated with the fuzzy regression model have been observed (Yu et al., 2005). First, in possibility analysis, Tanaka’s methodologies were extremely sensitive to outliers or high variabilities of data and they also ignored certain information contained in the data (Kim et al., 1996; Tedden and Woodal, 1994). Second, in necessity analysis, the necessity area cannot be obtained due to large variations in the data or an inappropriate model (Tanaka et al., 1989; Tanaka and Ishibuchi, 1992).
ISBN:1466554614
9781466554610
DOI:10.1201/b15337-19