Prediction Model of Net Photosynthetic Rate of Ginseng under Forest Based on Optimized Parameters Support Vector Machine
WU Hai-wei1, 2，YU Hai-ye1*，ZHANG Lei1
1. Key Laboratory of Bionics Engineering, Ministry of Education, Jilin University, Changchun 130022, China 2. School of Electrical and Information Engineering, Beihua University, Jilin 132021, China
Abstract：Using K-fold cross validation method and two support vector machine functions, four kernel functions, grid-search, genetic algorithm and particle swarm optimization, the authors constructed the support vector machine model of the best penalty parameter c and the best correlation coefficient. Using information granulation technology, the authors constructed P particle and ε particle about those factors affecting net photosynthetic rate, and reduced these dimensions of the determinant. P particle includes the percent of visible spectrum ingredients. ε particle includes leaf temperature, scattering radiation, air temperature, and so on. It is possible to obtain the best correlation coefficient among photosynthetic effective radiation, visible spectrum and individual net photosynthetic rate by this technology. The authors constructed the training set and the forecasting set including photosynthetic effective radiation, P particle and ε particle. The result shows that epsilon-SVR-RBF-genetic algorithm model, nu-SVR-linear-grid-search model and nu-SVR-RBF-genetic algorithm model obtain the correlation coefficient of up to 97% about the forecasting set including photosynthetic effective radiation and P particle. The penalty parameter c of nu-SVR-linear-grid-search model is the minimum, so the model’s generalization ability is the best. The authors forecasted the forecasting set including photosynthetic effective radiation, P particle and ε particle by the model, and the correlation coefficient is up to 96%.
武海巍1, 2，于海业1*，张 蕾1 . 基于参数优化支持向量机的林下参净光合速率预测模型 [J]. 光谱学与光谱分析, 2011, 31(05): 1414-1418.
WU Hai-wei1, 2，YU Hai-ye1*，ZHANG Lei1 . Prediction Model of Net Photosynthetic Rate of Ginseng under Forest Based on Optimized Parameters Support Vector Machine . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31(05): 1414-1418.
