Optimized Photosynthetic Active Radiation Prediction Model Based on Kernel Function Combination

Abstract:

Using the photosynthetic active radiation (PAR), scattering radiation (PFDdif) and direct radiation (PFDdir) from the ginseng base in forest as research object, a support vector machine model about photosynthetic active radiation based on linear function (k1), polynomial function (k2) and radial basis function (k3) was constituted. By using K-fold cross validation method, penalty parameter cand g numerical value were optimized by particle swarm optimization. Penalty parameter c and g numerical value of photosynthetic active radiation support vector machine model were configured 16 and 1 by grid search algorithm. 0.2k1+0.8k2 kernel function was chosed to construct the predict PAR model by related coefficient and the fitting equilibrium principle. Fitting index of predicting set 1 and 2 was separately 89.2132% and 81.7896% based on the predicting model. By using particle swarm algorithm, the two predicting models’ parameters were optimized. Fitting index of predicting set 1 and 2 was separately 92.1560% and 90.0360%. The predicting model based on 0.2k1+0.8k2 and particle swarm algorithm showed ability to predict PAR variation trend.