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

Abstract:

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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.

Info:

Periodical:

Edited by:

Zhenyu Du and Bin Liu

Pages:

204-207

DOI:

10.4028/www.scientific.net/AMM.65.204

Citation:

W. Tang and Y. Z. Zhao, "Structure of Mixture Kernel Function and its Application in Soft Measurement about Beating Degree", Applied Mechanics and Materials, Vol. 65, pp. 204-207, 2011

Online since:

June 2011

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$35.00

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