基于BA-LSSVM模型的月径流预测方法
发布时间：2018-12-14 15:18
【摘要】：针对最小二乘支持向量机模型传统参数选择方法费时且效果差的问题,利用蝙蝠算法的模型简单、快速收敛和全局搜索能力强的特点,优化模型的正则化参数和核函数参数,对水文时间序列建立最小二乘支持向量机预测模型。基于西江流域内的柳州水文站2000-2014年月径流资料对模型进行训练和预测,并与使用粒子群算法优化参数确定的最小二乘支持向量机模型,网格搜索及交叉验证优选参数确定的最小二乘支持向量机模型及BP神经网络模型进行比较。计算结果表明,基于蝙蝠算法优化最小二乘支持向量机模型具有很好的适用性和较高的预测精度,为利用最小二乘支持向量机模型解决非线性的水文时间序列问题提供了新的方向。
[Abstract]:Aiming at the problem that the traditional parameter selection method of least squares support vector machine (LS-SVM) is time-consuming and ineffective, the regularization parameters and kernel function parameters of the model are optimized by using the characteristics of the bat algorithm, such as simple model, fast convergence and strong global search ability. The prediction model of least squares support vector machine is established for hydrological time series. Based on the monthly runoff data of Liuzhou hydrologic station in Xijiang River Basin from 2000 to 2014, the model is trained and predicted, and the least squares support vector machine (LS-SVM) model is used to optimize the parameters by using particle swarm optimization (PSO). The least square support vector machine (LS-SVM) model and the BP neural network model are compared with each other. The results show that the optimization of least-squares support vector machine model based on bat algorithm has good applicability and high prediction accuracy. It provides a new direction for solving nonlinear hydrological time series problems using least square support vector machine (LS-SVM) model.
【作者单位】： 华北电力大学经济与管理学院;
【基金】：河北省社会科学基金项目(HB16YJ075)
【分类号】：P338;TP18
，
本文编号：2378831
[Abstract]:Aiming at the problem that the traditional parameter selection method of least squares support vector machine (LS-SVM) is time-consuming and ineffective, the regularization parameters and kernel function parameters of the model are optimized by using the characteristics of the bat algorithm, such as simple model, fast convergence and strong global search ability. The prediction model of least squares support vector machine is established for hydrological time series. Based on the monthly runoff data of Liuzhou hydrologic station in Xijiang River Basin from 2000 to 2014, the model is trained and predicted, and the least squares support vector machine (LS-SVM) model is used to optimize the parameters by using particle swarm optimization (PSO). The least square support vector machine (LS-SVM) model and the BP neural network model are compared with each other. The results show that the optimization of least-squares support vector machine model based on bat algorithm has good applicability and high prediction accuracy. It provides a new direction for solving nonlinear hydrological time series problems using least square support vector machine (LS-SVM) model.
【作者单位】： 华北电力大学经济与管理学院;
【基金】：河北省社会科学基金项目(HB16YJ075)
【分类号】：P338;TP18
，
本文编号：2378831
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