Climate model by SVM based on experienced knowledge in tobacco region division

Authors:
Wang Deji;Xu Bo;Li Guangcai;Chen Guoqun;Sui Bingyu
Affiliations:
Training Centre of National Tobacco Monopoly Bureau, Zhengzhou city, China and PetroChina Pipeline Research and Development Center, Langfang city, China and Institute of Intelligent Machines, Chin ...;PetroChina Pipeline Research and Development Center, Langfang city, China;Training Centre of National Tobacco Monopoly Bureau, Zhengzhou city, China;PetroChina Pipeline Research and Development Center, Langfang city, China;Institute of Intelligent Machines, Chinese Academy of Science, Hefei, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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Abstract

Tobacco region division is vital to improve the quality of the tobacco. And the climate model is the most important factor for the division. However, the climate variable, which was strongly corrupted by noises or fluctuations, can not be reconstructed by common method. In order to improve the performance of regression, the experienced knowledge about climate variable is incorporated in the training of SVM. The experimental results demonstrate the effectiveness and efficiency of our approach.