Formulation of Fuzzy Switching Regression Model

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  • ファジィスイッチング回帰モデルの構成(<特集>ソフトサイエンス)
  • ファジィスイッチング回帰モデルの構成
  • ファジィ スイッチング カイキ モデル ノ コウセイ

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Abstract

In the case where observed data are put in the analysis based on a regression model, the latent system of observed data is too complicated to be analyzed by the regression model. The same situations happen when the structures of the latent system are changed. In such a case an analyzer used to separate each of samples accordingly to the different structures and apply each of regression models to a group of the samples. In other words, when the given samples come out of several different systems, we should separate samples into several groups according to each of the latent systems and apply a regression model to each of samples. Without such a process we cannot obtain proper results from the given samples. Nevertheless, it is hard to separate samples according to each latent system in the case of multivariate data. Hitherto, there are many researches to investigate the structure under obtained data and analyze such data. J. C. Bezdek proposes Switching Regression Model based on Fuzzy Clustering Model to formulate a forecasting model. The model proposed by Bezdek is to separate mixed samples coming from plural latent systems and apply each regression model to the group of samples coming from each system. That is a Fuzzy c-Regression Model. In this paper, in order to deal with the possibility of a system, we employ a fuzzy regression model to build a Fuzzy Switching Regression Model. The fuzzy switching regression model is explained to analyze wholesale price indices in Japan.

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