DOI: 10.3724/SP.J.1004.2013.02143
Acta Automatica Sinica (自动化学报) 2013/39:12 PP.2143-2149
Abstract：
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 identi¯ed 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.
ReleaseDate：2014-07-21 17:04:32
Funds：National Natural Science Foundation of China (61203114), and Natural Science Foundation of Shaanxi Province (2013JM8029)