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Fuzzy Linear Regression

Ayse Tansu

ISBN 10: 3844384421 / ISBN 13: 9783844384420
Editorial: LAP Lambert Academic Publishing
Nuevos Condición: New Encuadernación de tapa blanda
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Librería en AbeBooks desde: 21 de mayo de 2012

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Descripción

Paperback. 220 pages. Dimensions: 8.7in. x 5.9in. x 0.5in.In real-life situation, complexity occurs when the system is not understandable. The size, shape, position, or color of a system can cause a phenomenon. These complexities are classified in fuzzy sets. The fuzzy sets role is significant when applied to complex phenomena, which is not easily described by traditional mathematics. Fuzzy regression may be more appropriate tool than the traditional regression analysis. In fuzzy regression, it is assumed that the systems structure is ambiguous or complex. A brief review of the linear programming models in fuzzy linear regression is given. Symmetric, asymmetric, trapezoidal and bell shaped membership functions are investigated. In addition, the different linear programming models are summarized, and a new model is presented for better results. Moreover, the new model is compared with other two models that are Tanaka and Hojati models. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. N° de ref. de la librería 9783844384420

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Detalles bibliográficos

Título: Fuzzy Linear Regression

Editorial: LAP Lambert Academic Publishing

Encuadernación: Paperback

Condición del libro:New

Tipo de libro: Paperback

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Sinopsis:

In real-life situation, complexity occurs when the system is not understandable. The size, shape, position, or color of a system can cause a phenomenon. These complexities are classified in fuzzy sets. The fuzzy sets role is significant when applied to complex phenomena, which is not easily described by traditional mathematics. Fuzzy regression may be more appropriate tool than the traditional regression analysis. In fuzzy regression, it is assumed that the system’s structure is ambiguous or complex.A brief review of the linear programming models in fuzzy linear regression is given. Symmetric, asymmetric, trapezoidal and bell shaped membership functions are investigated. In addition, the different linear programming models are summarized, and a new model is presented for better results. Moreover, the new model is compared with other two models that are Tanaka and Hojati models.

About the Author:

Ayşe Tansu is a Asst.Prof.Dr. in the Department of Industrial Engineering at Cyprus International Universtiy in North Cyprus.Her research interest include fuzzy sets, fuzzy regression analysis,warranty modelling and analysis and maintenance systems.She published several articles in international journals.

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