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Decision Making by a Fuzzy Regression Model with Modified Kernel

Authors:
Kiyoshi Nagata
Michihiro Amagasa

Keywords: Fuzzy regression model; Kernel method; Decision

Abstract:
Regression model is a popular and powerful model for finding a rule from large amount of collected data. It is widely used in various areas for predicting the value derived from observable values. Especially in multivariate numerical analysis, several types of regression models, not only linear but also polynomial or exponential, are established. In case of nonnumerical data, although fuzzy regression models are proposed and investigated by some researchers, most of them are linear models. In order to construct a non-linear regression model with fuzzy type data set, new type of devices are needed since fuzzy numbers have a complicated behavior in multiplication and division. In this paper, we try to extend a linear fuzzy regression model to non-linear model by adapting a modified kernel method.

Pages: 18 to 23

Copyright: Copyright (c) IARIA, 2017

Publication date: June 25, 2017

Published in: conference

ISSN: 2326-9332

ISBN: 978-1-61208-566-1

Location: Venice, Italy

Dates: from June 25, 2017 to June 29, 2017