Modeling a Large-Scale Nonlinear System Using Adaptive Takagi-Sugeno Fuzzy Model on PCA Subspace

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• 作者：Jialin Liu
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2007
• 出版时间：January 31, 2007
• 年：2007
• 卷：46
• 期：3
• 页码：788 - 800
• 全文大小：462K
• 年卷期：v.46,no.3(January 31, 2007)
• ISSN：1520-5045

文摘

A data-driven Takagi-Sugeno fuzzy model is developed for modeling a real plant situation with the dependentinputs and the nonlinear and time-varying input-output relation. The collinearity of inputs can be eliminatedthrough the principal component analysis. The TS model split the operating regions into a collection of IF-THEN rules. For each rule, the premise is generated from clustering the compressed input data, and theconsequence is represented as a linear model. A post-update algorithm for model parameters is also proposedto accommodate the time-varying nature. Effectiveness of the proposed model is demonstrated using realplant data from a polyethylene process.