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A weighted LS-SVM approach for the identification of a class of nonlinear inverse systems

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Abstract

In this paper, a weighted least square support vector machine algorithm for identification is proposed based on the T-S model. The method adopts fuzzy c-means clustering to identify the structure. Based on clustering, the original input/output space is divided into several subspaces and submodels are identified by least square support vector machine (LS-SVM). Then, a regression model is constructed by combining these submodels with a weighted mechanism. Furthermore we adopt the method to identify a class of inverse systems with immeasurable state variables. In the process of identification, an allied inverse system is constructed to obtain enough information for modeling. Simulation experiments show that the proposed method can identify the nonlinear allied inverse system effectively and provides satisfactory accuracy and good generalization.

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Authors and Affiliations

  1. School of Automation, Southeast University, Nanjing, 210096, China

    ChangYin Sun & ChaoXu Mu

  2. College of Electrical Engineering, Hohai University, Nanjing, 210098, China

    XunMing Li

Authors
  1. ChangYin Sun
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  2. ChaoXu Mu
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  3. XunMing Li
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Corresponding author

Correspondence to ChangYin Sun.

Additional information

Supported by the National Natural Science Foundation of China (Grant No. 60874013) and the Doctoral Project of the Ministry of Education of China (Grant No. 20070286001)

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Sun, C., Mu, C. & Li, X. A weighted LS-SVM approach for the identification of a class of nonlinear inverse systems. Sci. China Ser. F-Inf. Sci. 52, 770–779 (2009). https://doi.org/10.1007/s11432-009-0097-6

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  • DOI: https://doi.org/10.1007/s11432-009-0097-6

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