群智能优化算法优化支持向量机的方法及应用

Method and application of swarm intelligence optimization algorithm for support vector machine optimization

水质评价过程具有多变量、非线性、不确定等特点,传统的粒子群算法训练神经网络的水质评价模型收敛速度慢、泛化性能差。为了克服传统模型的缺点,提出了利用动态多种群粒子群算法训练支持向量机的模型,并利用多种群粒子群算法优化支持向量机结构参数。该模型结合了粒子群算法的搜索性能以及支持向量机的高效性、强鲁棒性等优点,提高了模型的泛化能力。通过对新疆某流域站点的水文数据进行仿真,结果得出该方法的相对误差为2.74%,远低于传统粒子群算法4.21%的相对误差,由此证明该模型的应用效率及精度得到提高,适用于日常水质评价工作。

The process of water quality assessment is multivariable,nonlinear and uncertain.The traditional particle swarm optimization training neural network water quality evaluation model has slow convergence speed and poor generalization performance.In order to overcome the shortcomings,this paper proposes a new model to use dynamic multigroup particle swarm optimization algorithm to train support vector machine,in which the DMPSO is used to optimize the parameters of the fuzzy neural network model.The model combines the search performance of PSO algorithm,the efficiency and robustness of SVM,which can improve the generalization ability of the model.Through the simulation experiment on the hydrological data,the results show that the relative error of this model is 2.74%,which is much lower than the 4.21%relative error of the traditional particle swarm optimization.It is proved that the efficiency and accuracy of the model are improved,and is suitable for the daily water quality evaluation.