A self-organizing algorithm for T-S fuzzy model based on support vector machine regression and its application

被引：0
作者：
机构：
[1] School of Automation and Information Engineering, Xi'an University of Technology
来源：
Liang, Y.-M. (liangym@xaut.edu.cn) | 1600年 / Science Press卷 / 39期
关键词：
Air preheater; Clustering; Single crystal furnace; Support vector machine regression; T-S fuzzy model;
D O I：
10.3724/SP.J.1004.2013.02143
中图分类号：
学科分类号：
摘要：
A new self-organizing algorithm for T-S fuzzy model is proposed by combining the fuzzy clustering algorithm and the support vector machine (SVM) regression algorithm. This algorithm firstly uses an improved fuzzy clustering algorithm to extract fuzzy rules and identify antecedent parameters. Then the T-S fuzzy model consequent is transformed into a standard linear support vector machine regression model, thus its parameters are identified using the support vector machine regression algorithm. Simulation results show that the self-organizing algorithm for T-S fuzzy model in this paper still has higher approximation accuracy and better generalization ability in the case of a small number of rules compared with the existing self-organizing algorithm. Finally, a heater temperature model of Czochralski single crystal furnace and an air preheater temperature model are better established using the proposed self-organizing algorithm for T-S fuzzy model. Copyright © 2013 Acta Automatica Sinica. All rights reserved.
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页码：2143 / 2149
页数：6
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