网络发布日期：2016-05-13
中文摘要:Van Genuchten模型(简称VG模型)是目前运用最为广泛的土壤水分特征曲线模型，提出适宜的优化算法进行模型参数识别也是一个非常重要的研究方向。针对标准的粒子群算法易陷入局部最优的缺点，给出了一种多邻域粒子群算法，可以有效地克服粒子群算法易陷入局部最优的缺点，并利用该算法对VG模型参数进行识别，最后用所求解的参数对不同类型土壤持水性能进行了试验。数值实验结果表明，多邻域粒子群算法能够有效地应用于VG模型的参数识别，与其它算法相比在性能和精度上都有所提高，而且对参数的取值范围也可以较大地放宽。因此，多邻域粒子群算法可以作为VG模型参数识别的一种新方法。
Abstract:The Van Genuchten model (hereinafter refer to as VG model) is the most wide use model for soil water characteristic curve at present, put forward the feasible optimization algorithm to identify the model parameters is also a very important research direction. In this paper, pointed to the disadvantage of the standard particle swarm algorithm was easy to fall into local optimum, presented a multiple neighborhood particle swarm algorithm which can be effectively overcome the shortcoming of local optimum, also can use this algorithm to identify the parameters of the VG model, finally different type of soil moisture performances are tested by these parameters. The numerical experiment results showed that: The multiple neighborhood particle swarm algorithm can be effectively applied to identify the parameters of the VG model, compared with other algorithms in terms of performance and precision are improved, also the scope of parameters can be larger. As a result, The multiple neighborhood particle swarm algorithm can be used as a new kind of method for identification of the VG model parameters.
keywords:soil water characteristics curve Van Genuchten model parameter identification particle swarm algorithm multiple neighborhood particle swarm algorithm
文章编号：
中图分类号：
文献标志码：
基金项目:国家自然科学基金（11171043）
引用文本：
高雄飞，刘元会，郭建青，王媛英，郝立瑛.土壤水分特征曲线模型参数识别的多邻域粒子群算法[J].中文模板102,2014,(6):48-52.
图/表