基于多算法优化SVM的粗糙面参数反演

支持向量机（SVM）是粗糙面参数反演中常用的一种反演算法，SVM反演中的惩罚参数C和核函数参数G对反演结果精度的影响较大，若参数取值不当，会使模型产生"过学习"或者"欠学习"的现象，从而降低预测精度.给出几种SVM参数C和参数G的优化算法，如K折交叉验证（K-CV）、遗传算法（GA）和粒子群算法（PSO），并在此基础上提出一种基于K-CV和GA改进的PSO算法（GA-CV-PSO）.利用矩量法（MoM）获得的粗糙面后向散射系数构造训练集和测试集，通过不同参数反演的仿真结果对比不同优化算法的反演精度和计算时间，表明GA-CV-PSO算法克服了单一优化算法的缺陷，具有更精确的反演精度和更强的泛化能力.

Parameter Inversion of Rough Surface Optimization Based on Multiple Algorithms for SVM

Support vector machine (SVM) is one of the most widely used algorithms in parameter inversion of rough surface. However, the penalty parameters (C) and kernel function parameters (G) in SVM affects accuracy of results. If the parameters are not used properly, the model will lead to "over learning"or "less learning", which reduces greatly prediction accuracy. Several optimization algorithms for C and G of SVM are shown, such as K-fold cross validation (K-CV), genetic algorithm (GA) and particle swarm optimization (PSO). An improved PSO algorithm based on K-CV and GA (GA-CV-PSO) is proposed. The training set and test set are constructed with rough surface backscattering coefficient obtained by the moment method (MoM). Inversion precision and calculation time of different optimization algorithms are compared. It shows that GA-CV-PSO algorithm overcomes shortcomings of single optimization algorithms, with more accurate inversion precision and stronger generalization ability.