基于MSCPSO混合核SVM参数优化的生菜品质检测

Detection of Lettuce Quality Based on Parameters Optimization of MSCPSO Mixed Kernel SVM

摘要:

核函数形式的选择与核函数参数值的大小是影响支持向量机的2个关键因素，传统的支持向量机分类精度低、时效性差，为了获得高精度、高时效性的支持向量机，从影响支持向量机的核函数与核函数参数值2个关键因素着手，提出了基于变尺度混沌粒子群优化（MSCPSO）混合核SVM参数的分类器。将此分类模型用于预测生菜叶片的生育期，以及预测3个生育期的生菜叶片氮素水平，预测精度分别达到91.51%、85.38%、82.59%和81.26%。与传统的粒子群优化混合核SVM的分类器和变尺度混沌粒子群优化RBF_SVM分类器相比，提出的分类器模型分类精度高、时效性好。

Abstract:

The traditional support vector machine has two faults: low classification accuracy and poor timeliness. In order to obtain support vector machine (SVM) with high accuracy and efficiency, the parameter optimization of SVM with mixed kernels based on mutative scale chaos particle swarm optimization (MSCPSO) was presented. This model was used to predict the growth stage of lettuce leave, which was consist of seedling stage, tillering stage and mature stage, and N content levels of three growth periods respectively. The prediction accuracy achieved to 91.51%, 85.38%, 82.59% and 81.26%. Compared with the traditional particle swarm optimization mixed nuclear SVM classifier and mutative scale chaos particle swarm optimization RBF_SVM classifier, the proposed classifier model showed higher classification accuracy and timeliness.