Robust Possibilistic C-Regression Models Algorithm
2017 4TH INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT)(2017)
摘要
This paper studies the problem of the parameter identification based on fuzzy c-regression models for nonlinear systems. The novel procedure combines the possibilistic c-means procedure with fuzzy c-regression models (FCRM) in order to reduce the effects of noisy data. In comparison to the existing algorithms in the literature, the proposed method utilizes a generalized objective function that reduces the errors of partitioning data sets contaminated by noise and as a consequence an accurate model is obtained. The results of this study demonstrate the effectiveness of proposed method compared with other extended versions of FCRM algorithm.
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关键词
Fuzzy c-regression model, Possibilistic c-means, robust clustering
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