唐江凌,蔡从中,肖婷婷,皇思洁

• 1:重庆大学物理学院应用物理系

摘要(Abstract)：

根据Zr-2合金的晶粒尺寸在不同热工艺参数(变形温度、变形程度、变形速率)下的12组实测数据,应用基于粒子群算法寻找最优参数的支持向量回归方法,建立了合金晶粒尺寸的预测模型。通过与模糊神经网络模型的结果进行比较,结果表明:基于相同的试验样本,支持向量回归预测模型的平均绝对误差和平均绝对百分误差都比模糊神经网络预测模型的小,而复相关系数大。这说明,支持向量回归预测模型预测精度比模糊神经网络模型要高,是简单而精确的建模方法,可用于优化热加工参数。

关键词(KeyWords)： 支持向量机;模糊神经网络;粒子群择优;Zr-2合金;晶粒尺寸

Abstract:

Keywords:

基金项目(Foundation): 教育部新世纪优秀人才支持计划(NCET-07-0903);; 教育部留学回国人员科研启动基金(教外司留[2008]101-1);; 中央高校基本科研业务费(CDJXS11101135)

作者(Author): 唐江凌,蔡从中,肖婷婷,皇思洁

DOI: 10.13289/j.issn.1009-6264.2013.02.002

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