文章基本信息

标题：A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization
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作者：Moêz Soltani ; Abdelkader Chaari ; Fayçal Ben Hmida 等
期刊名称：International Journal of Applied Mathematics and Computer Science
电子版ISSN：2083-8492
出版年度：2012
卷号：22
期号：3
DOI：10.2478/v10006-012-0047-0
出版社：De Gruyter Open
摘要：This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed methodology is based on adding a second regularization term in the objective function of a Fuzzy C-Regression Model (FCRM) clustering algorithm in order to take into account noisy data. In addition, a new error measure is used in the objective function of the FCRM algorithm, replacing the one used in this type of algorithm. Then, particle swarm optimization is employed to finally tune parameters of the obtained fuzzy model. The orthogonal least squares method is used to identify the unknown parameters of the local linear model. Finally, validation results of two examples are given to demonstrate the effectiveness and practicality of the proposed algorithm.
关键词：Takagi–Sugeno fuzzy model; noise clustering algorithm; fuzzy c-regression model; orthogonal least squares; particle swarm optimization