Structure of Mixture Kernel Function and its Application in Soft Measurement about Beating Degree

Wei Tang; Yan Zi Zhao

doi:10.4028/www.scientific.net/AMM.65.204

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 65Structure of Mixture Kernel Function and its...

Structure of Mixture Kernel Function and its Application in Soft Measurement about Beating Degree

Abstract:

Kernel function is dominant in regression process of Support Vector Machine (SVM), it influences the prediction performance of SVM directly. Single local or global kernel function has limitation in generalization or learning capacity. The mixture kernel function with two good capacities had been structured, and it had been used in soft-sensor modeling of refining process. The contrast of simulation results show: the generalization ability of SVM based on mixture kernel function is better than the ones based on the single local or global kernel function, the algorithm had been applied in the refining process of HETAO wood industry factory, and the prediction result meets the requirement of craft production.

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Periodical:

Applied Mechanics and Materials (Volume 65)

Pages:

204-207

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.65.204

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Online since:

June 2011

Authors:

Wei Tang, Yan Zi Zhao

Keywords:

Beating Degree, Kernel Function, Soft Measurement, Support Vector Machine (SVM)

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References

[1] Fen Zhang, Liang Tao, Yan Sun. SVM and Its Application Based on Mixtures of Kernels [J]. Computer Technology and Development, 2006, 16(2): 176-178.

Google Scholar

[2] Jinfeng Chen, Huizhong Yang. Application of Mixture Kernels SVM in Chemical Industry Soft Sensing [J]. Control and Instruments in Chemical Industry, 2008, 35(2): 36-38.

Google Scholar

[3] SMITS G F, JORDAAN E M. Improved SVM Regression Using Mixtures of Kernels [C], Proceedings of the 2002 International Joint Conference on Neural Networks. Hawaii: IEEE, 2002, 3(2): 2785 - 2790.

DOI: 10.1109/ijcnn.2002.1007589

Google Scholar

[4] Haiping Huang. Performance and Process on Double Disc Refiner [J]. HEI NONG JIANG Pulp&Paper. 2002. 1.

Google Scholar

[5] Jinshou Yu. Soft Sensing Technology and Its Application [J]. Process Automation Instrumentati- on, 2008, 29(1): 1- 6.

Google Scholar

[6] Dingcheng Wang, Tingjian Fang, Li fu Gao etc. Support Vector Machines regression on-line modeling and its application [J], CONTROL AND DECISION, 2003, 18(1): 89- 91.

Google Scholar