运筹与模糊学

Vol.1 No.2 (November 2011)

基于遗传算法优选参数的灰色LS-SVM预测
Grey LS-SVM Forecasting with Parameter Optimized by Genetic Algorithm

 

作者:

周德强

 

关键词:

灰色LS-SVMGM(1 1)模型遗传算法参数优选小样本预测Grey Least Square Support Vector Machines GM (1 1) Model Genetic Algorithms Parameter Selection Small Samples Forecasting

 

摘要:

利用灰色预测方法中累加生成运算形成累加数据,将累加数据作为训练样本构造灰色LS-SVM,并利用遗传算法对灰色LS-SVM自身的参数进行优选,然后将基于遗传算法优选参数的灰色LS-SVM用于小样本预测。选取了典型例子进行验证,并与传统GM(1, 1)和LS-SVM方法进行对比。结果表明本文所提出的方法预测效果良好,且预测模型具有更好的泛化能力。

This paper utilized the accumulation generation operation of grey prediction to produce accumulated data, and accumulated data were used to construct grey LS-SVM. At the same time the parameters for LS-SVM were pretreated through genetic algorithms to get the optimum parameter values, then the optimized LS-SVM based on genetic algorithms was used to small samples forecasting. A typical example was taken to be analyzed and compared with GM (1, 1) and LS-SVM method. The result shows that the method forecast effect is better, and the prediction model has better generalization ability.

文章引用:

周德强 (2011) 基于遗传算法优选参数的灰色LS-SVM预测。 运筹与模糊学, 1, 29-33. doi: 10.12677/orf.2011.12006

 

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