Research on Forecasting Method Based on Genetic Algorithms and Support Vector Machines
Research on Forecasting Method Based on Genetic Algorithms and Support Vector Machines
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基于遗传算法和支持向量机的预测方法研究
DOI：
10.4028/www.scientific.net/amm.29-32.2603
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发表时间：
2010-08
期刊：
影响因子：
--
通讯作者：
Guo, Pengyan
中科院分区：
文献类型：
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作者：
Xiao, Chengyong;Feng, Zhipeng;Guo, Pengyan
基于支持向量机的机器状态预测在样本稀少的情况下具有良好的泛化能力。支持向量机的参数选择对支持向量机的学习效果和泛化能力至关重要。此外，嵌入维数影响非线性系统的相空间重构，以及机器状态预测的精度。提出了一种基于遗传算法的支持向量机参数和嵌入维数优化方法。将该模型应用于电铲电传动系统的振动趋势预测。结果表明，该方法避免了人工选择参数的盲目性，同时大大提高了预测性能。
State forecast of machine using support vector machines has good generalization ability in situation of rare samples. Appropriate parameter selection is very crucial to the learning results and generalization ability of support vector machines. In addition, embedding dimension influences the phase space reconstitution of nonlinear systems, as well as the precision of machine state forecasting. In this paper, an approach to optimize the parameters of SVM and the embedding dimension based on genetic algorithms was proposed. The proposed model is applied to the tendency forecasting of the vibration of shovel electric drive system. The results show that it can avoid blindness of manually selection of parameters and meanwhile improves the prediction performance greatly.