Soft-Sensor Method Based on Least Square Support Vector Machines Within Bayesian Evidence Framework

Authors:
Wei Wang;Tianmiao Wang;Hongxing Wei;Hongming Zhao
Affiliations:
Beijing University of Aeronautics and Astronautics, Beijing 100083, P.R. China;Beijing University of Aeronautics and Astronautics, Beijing 100083, P.R. China;Beijing University of Aeronautics and Astronautics, Beijing 100083, P.R. China;Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100039, P.R., China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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Abstract

Based on the character and requirement of the dynamic weighing of loader, the soft sensor technique was adapted as the weighing method, and the least square support vector machine (LS-SVM) as its modelling method. Also the Bayesian evidence framework was used in LS-SVM for selecting and tuning its parameter. And then, after the nonlinear regression algorithms of LS-SVM and the principle of Bayesian evidence framework were introduced, the soft sensor model based on LS-SVM was given. In the end, emulation analysis results indicate that soft-sensor method based on LS-SVM within Bayesian evidence framework is a valid means for solving dynamic weighing of loader.