Improved Empirical Eigenfunctions Based Model Reduction for Nonlinear Distributed Parameter Systems

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• 作者：Mian Jiang ; Hua Deng
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2013
• 出版时间：January 16, 2013
• 年：2013
• 卷：52
• 期：2
• 页码：934-940
• 全文大小：369K
• 年卷期：v.52,no.2(January 16, 2013)
• ISSN：1520-5045

文摘

Karhunen鈥揕o猫ve (KL) decomposition is a popular approach for determining the principal spatial structures from the measured data. Empirical eigenfunctions (EEFs) can generally generate a relatively low-dimensional model among all linear expansions. The current study proposes improved EEFs for model reduction of the nonlinear distributed parameter systems (DPSs) by the basis function transformation from initial EEFs. The basis function transformation matrix is obtained using the balanced truncation method. This performance is proved theoretically. The numerical simulations for the rescaled Kuramoto鈥揝ivashinsky equations show that using the improved EEFs has an evidently better performance than using the same number of the initial EEFs.