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Fuzzy rule-based support vector regression system

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Abstract

In this paper, we design a fuzzy rule-based support vector regression system. The proposed system utilizes the advantages of fuzzy model and support vector regression to extract support vectors to generate fuzzy if-then rules from die training data set. Based on the first-order linear Tagaki-Sugeno (TS) model, the structure of rules is identified by the support vector regression and then the consequent parameters of rules are tuned by the global least squares method. Our model is applied to the real world regression task. The simulation results gives promising performances in terms of a set of fuzzy rules, which can be easily interpreted by humans.

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Authors and Affiliations

  1. Information Engineering School, University of Science and Technology, 100083, Beijing, China

    Ling Wang, Zhichun Mu & Hui Guo

Authors
  1. Ling Wang
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  2. Zhichun Mu
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  3. Hui Guo
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Additional information

This paper was supported by the National High Technology Research and Development Program of China 863 program (No. 2002 AA412010) , the Technology development Program of the Science and Technology Ministry of China (No. 2003EG113016), and the key discipline construction program of Beijing Municipal commission of education.

Ling WANG is a Ph. D. candidate at the Institute of Information Engineering, University of Science and Technology, Beijing. Her current research areas include artificaial intelligent, machine learning and data mining.

Zhichun MU is a Professor at the University of Science and Technology, Beijing. His research interests include the artificial intelligent control and machine learning.

Hui GUO is a Ph.D. candidate at the Institute of Information Engineering, University of Science and Technology Beijing. His current research areas include amchine learning and data mining.

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Wang, L., Mu, Z. & Guo, H. Fuzzy rule-based support vector regression system. J. Control Theory Appl. 3, 230–234 (2005). https://doi.org/10.1007/s11768-005-0040-3

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  • DOI: https://doi.org/10.1007/s11768-005-0040-3

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