The Metamer Number Prediction Based on Improved SVM
WANG DeJi1,2, XIONG FanLun1, WANG RuJing1, ZHA ShiHong1,2
1.Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031 2.Department of Automation, University of Science and Technology of China, Hefei 230026
Abstract The relation between the temperature and the metamer is very important for the virtual plant growth model. However, it is difficult to predict it just by SVM because there are too many noises in the raw data. In this paper, a new kernel function based on the information geometry is established to overcome the high noise and nonlinear data. The relation between number of metamer and temperature can thus be gotten precisely. The method is applied to the cotton growth model. Compared with the methods of least square and SVM, the improved SVM can predict the number of metamer more precisely.
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2006, Vol. 19 
