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Parameter selection method for support vector machine based on adaptive fusion of multiple kernel functions and its application in fault diagnosis

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Abstract

A new model parameter selection method for support vector machine based on adaptive fusion of multiple kernel functions is proposed in this paper. Characteristics of local kernels, global kernels, mixtures of kernels and multiple kernels were analyzed. Fusion coefficients of the multiple kernel function, kernel function parameters and regression parameters are combined to form the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. Then, we use a fifth-degree cubature Kalman filter to estimate the parameters. In this way, we realize adaptive selection of the multiple kernel function weighted coefficient, the kernel parameters and the regression parameters. A simulation experiment was performed to interpret the PE process for fault diagnosis.

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Acknowledgements

This work was supported by the Natural Science Foundation of China (61403229, 61503213) and Zhejiang Provincial Natural Science Foundation of China (LY13F030011, LQ17F030005).

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Authors and Affiliations

  1. Logistics Engineering College, Shanghai Maritime University, Shanghai, 200000, People’s Republic of China

    Hailun Wang

  2. College of Electrical and Information Engineering, Quzhou University, Quzhou, 324000, People’s Republic of China

    Hailun Wang & Daxing Xu

  3. School of Computing, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK

    Alexander Martinez

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  1. Hailun Wang
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  2. Daxing Xu
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  3. Alexander Martinez
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Correspondence to Hailun Wang.

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Wang, H., Xu, D. & Martinez, A. Parameter selection method for support vector machine based on adaptive fusion of multiple kernel functions and its application in fault diagnosis. Neural Comput & Applic 32, 183–193 (2020). https://doi.org/10.1007/s00521-018-3792-7

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