膨胀土等级判别的遗传支持向量机多分类方法

针对支持向量机模型中的参数难以确定的状况，提出了遗传支持向量机方法，即利用遗传算法来搜索支持向量机与核函数的参数，避免了人为选择参数的盲目性，同时提高了支持向量机的推广预测能力，并将该方法应用于膨胀土胀缩等级的判别分类问题。考虑影响膨胀土判别的重要因素，选用液限、胀缩总率、塑性指数、天然含水量和自由膨胀率5个指标作为模型的判别因子，以4类膨胀土胀缩等级作为相应的输出，以膨胀土实测数据作为学习样本进行训练，建立相应分类函数对待判样本进行分类。研究结果表明：遗传支持向量机模型分类性能良好，预测精度高，是膨胀土胀缩等级分类的一种有效方法，可以在实际工程中进行推广。

Multi classification Method of GA SVM on Identifying Grade of Expansive Soils

Aiming at the fact that parameters in support vector machine（SVM） model were difficult to be identified, a genetic algorithm SVM（GA-SVM） was proposed to avoid the blindness in parameter choosing and improve the estimation ability of SVM, in which the parameters in SVM and kernel function were searched by genetic algorithm. And it was then applied to the classification for the swell and shrink grade of expansive soils. Five indexes including liquid limit, total swell-shrink ratio, plasticity index, water contents and free expansive ratio were adopted as discriminated factors. And the four grades of the expansive soils were the outputs correspondingly. Classification function was obtained through training a large set of expansive samples. And it was shown that the classification method of GA-SVM was effective and with high accuracy.