改进PSO的SVM回归模型及在气温预测中的应用

兰州市气温是一个非平稳序列,具有典型噪声大、不稳定的特征,气温变化越大,越不稳定.为了能够提高支持向量机在气温预测中的预测精度、强化泛化能力和降低参数选择的灵敏度.本文提出了改进的粒子群算法(improved particle swarm optimization, IPSO)优化支持向量机(support vector machine, SVM)的气温预测模型.首先在粒子群算法(particle swarm optimization, PSO)中引入了自适应惯性权重以提高PSO算法的全局寻优能力和局部开发能力,其次利用改进的IPSO算法优化SVM的惩罚因子和核函数参数,将优化后的模型(IPSO-SVM)应用于气温预测中.以兰州地面观测站点实际数据作为样本数据,运用Matlab实验工具进行训练和预测,实验结果表明,本文IPSO-SVM模型相比于BP, SVM, GRID-SVM, GWO-SVM, ABC-SVM, ACO-SVM模型具有更强的泛化能力,更好的拟合度,可以更加准确地预测气温的变化,进一步验证了该模型在气温预测方面的可行性.

Improved PSO SVM Regression Model and Its Application in Temperature Prediction

The temperature of Lanzhou City is a non-stationary sequence with typical characteristics of loud noise and instability. As the temperature changes greatly, the sequence gets unstable. In order to improve the prediction accuracy, strengthen the generalization ability, and reduce the sensitivity of parameter selection of support vector machine (SVM) in temperature prediction, in this study, the improved particle swarm optimization (IPSO) algorithm is proposed to optimize the temperature prediction model of SVM. Firstly, the adaptive inertia weight is introduced into the particle swarm optimization (PSO) algorithm to improve the global optimization ability and local development ability of the PSO algorithm. Secondly, the improved IPSO is used to optimize the penalty factor and kernel function parameter of the SVM, and the optimized model (IPSO-SVM) is applied to the temperature prediction. The actual data of Lanzhou ground observation station are taken as sample data, and Matlab experimental tools are used for training and prediction. The experimental results show that the IPSO-SVM model in this study has a stronger generalization ability and better fitting degree than back propagation (BP), SVM, GRID-SVM, GWO-SVM, ABC-SVM, and ACO-SVM. It can predict the change in temperatures more accurately, which further verifies the feasibility of this model in temperature prediction.