基于改进SVM的叶元数目预测

The Metamer Number Prediction Based on Improved SVM

根据外界温度预测叶元数目在建立虚拟植物生长模型中有着重要意义。但是由于环境存在高噪声,不能通过简单的SVM或者最小二乘进行回归预测。本文从信息几何角度,构造具有数据依赖性的核函数,克服建模数据的高噪声、非线性,从而能准确预测叶元数目与温度函数关系。最后把模型应用于棉花生长模型的叶元预测,并和标准SVM、最小二乘进行比较。实验证明新模型在准确度上有较大提高。

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.