Method of identifying a type 2 membership function and application to decision-making problems

• Autorzy: Uemura Y.
• Języki publikacji: EN

Abstrakty

EN

Tanaka (1991) suggested that the parameters of a linear regression model should be made fuzzy In order to better reflect the nature of the system, involving a definite degree of variability, and created a fuzzy linear regression model. This model can be formulated in the form of a linear programming problem that minimizes the span between the upper and lower limits under the constraints that include all data. In recent years, all the attention in this context has been focused on a fuzzy number that has an indifferent zone. A fuzzy number that we consider here is defined by using a type 2 membership function. This paper addresses the fact that a type 2 membership function has the upper and lower limits and shows that a type 2 membership function can be identified by expanding a fuzzy linear regression model into a fuzzy linear polynomial regression model. Finally, after a proposed fuzzy polynomial model is identified, a mathematical model is developed for a fuzzy decision-making method that accounts for an indifferent zone.

Słowa kluczowe

EN

type 2 membership function; fuzzy linear regression model; fuzzy log-linear regression model; fuzzy linear polynomial regression model; indifferent zone; decision rule on a fuzzy event

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2015
• Tom: Vol. 44, no. 3
• Strony: 399--406
• Opis fizyczny: Bibliogr. 15 poz., rys.

Twórcy

autor

Uemura Y.

• Faculty of Education, Mie University, 1515 Kamihamacho, Isu; Mie, Japan

Bibliografia

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