摘要
讨论了嵌入维数d和时间延迟?作为空间重构参数对LS-SVM预测模型精度的影响,提出了基于PSO参数优化的LS-SVM预测方法。将d、?以及模型参数(正则化参数?、核函数宽度?)作为优化对象,利用PSO方法对4个参数共同优化选取,建立LS-SVM风速预测模型。对2组风速数据进行了实验研究,结果显示该方法预测误差约为5.79%和7.33%。而对比方法 (单纯优化?、?)的误差为8.22%和11.10%。这一结果表明,同时对d、?、?、?进行优化选取是有必要的,相对于单纯优化?、?的模型,该方法可以大大提高预测模型精度。
The impact on LS-SVM prediction model accuracy of the embedding dimension d and time delay τ were studied, which were used as a space reconstruction parameter. And LS-SVM prediction method based on PSO parameter optimization was proposed. In this method, particle swarm algorithm was used to optimize the embedding dimension d, time delay τ and other model parameters（regularization parameter γ, kernel function parameter σ）, and then established prediction model. 2 groups of wind speed were predicted by using this method. The prediction error is about 5.79% and 7.33%, and the error of the contrast method（optimize γ, σ only） is 8.22% and 11.10%. The results show that the optimal selection of d,τ, γ, σ is necessary. Compare with the comparison model, this method can greatly improve the prediction accuracy.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2016年第23期6337-6342,6598,共6页
Proceedings of the CSEE
基金
中央高校科研业务费项目(2015MS102)~~
