标题:
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
关键字:
灰色LS-SVM, GM(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
期刊名称:
《Operations Research and Fuzziology》, Vol.1 No.2, 2011-11-25
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.