地下水化学特征组分识别的粒子群支持向量机方法

The particle swarm optimization support vectors machine method of identifying standard components of ions of groundwater

采用粒子群算法优化支持向量机参数,建立了地下水化学特征组分识别的粒子群支持向量机模型.该方法利用支持向量机结构风险最小化原则和粒子群快速全局优化特点,通过对水源样本的学习,可以快速自动建立典型离子化学组分含量与其所属岩层类别之间的映射关系.

The parameters of support vector machine was optimized by using particle swarm optimization, then the pattern recognition model of identifying standard components of ions of groundwater was constructed. The method takes advantages of the minimum structure risk of SVM （ support vectors machine） and the quickly globally optimizing ability of PSO （ particle swarm optimization）, the mapping relation between the contents of typical components of groundwater ions and the geology hydrological classes may be set up quickly, by learning from samples of water resources.