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Neural Computing and Applications

, Volume 32, Issue 1, pp 183–193 | Cite as

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

  • Hailun WangEmail author
  • Daxing Xu
  • Alexander Martinez
Brain- Inspired computing and Machine learning for Brain Health
  • 86 Downloads

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.

Keywords

Support vector regression machine Kernel function characteristics Parameter selection Fifth-degree cubature Kalman filter 

Notes

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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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Logistics Engineering CollegeShanghai Maritime UniversityShanghaiPeople’s Republic of China
  2. 2.College of Electrical and Information EngineeringQuzhou UniversityQuzhouPeople’s Republic of China
  3. 3.School of ComputingNewcastle UniversityNewcastle upon TyneUK

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