Al基非晶合金表征参数的支持向量回归分析  

Parameters analysis of Al-based amorphous alloys using support vector regression

作　　者：徐燕[1] 张玉凤[1] 高湉 张研 张惠然[2] 刘永生[1] 

机构地区：[1]上海电力学院数理学院,上海201399 [2]上海大学计算机工程与科学学院,上海200444

出　　处：《中国有色金属学报》2016年第4期836-843,共8页The Chinese Journal of Nonferrous Metals

基　　金：上海市教委科研创新资助项目(12ZZ174);上海市教育发展基金会晨光计划资助项目(12CG63)~~

摘　　要：根据一系列Al基非晶合金薄带实测数据集,应用粒子群优化支持向量回归方法(PSO-SVR),建立一个通过相关表征参数来预测Al基非晶合金晶化温度(Tx)的模型。利用该模型对不同类型铝基非晶合金的晶化温度(Tx)进行建模和预测研究,并与反向传播神经网络(BPNN)预测方法进行比较。结果表明:基于留一交叉验证法(LOOCV)的PSO-SVR模型预测的晶化温度误差要比BPNN模型预测的小得多,这说明模型中所采用的特征参数能很好地描述该系列Al基非晶合金的晶化行为和热稳定性。According to the experimental data of Al-based amorphous alloys, a model to predict the crystallization temperature Tx of Al-based amorphous alloys by using particle swarm optimization combined with support vector regression(PSO-SVR) was established. Based on this model, crystallization temperature Tx can be predicted, and then compared with the method of back-propagation neural network(BPNN). The results show that the prediction error is smaller by using PSO-SVR. This means that the crystallization behavior and thermal stability of Al-based amorphous alloys can be well described by the parameters used in PSO-SVR model. Moreover, the PSO-SVR model could provide an important theoretical and practical guidance to the research on Al-based amorphous alloys.

关 键 词：Al基非晶合金 晶化温度 支持向量回归 粒子群优化 

分 类 号：TG139.8[一般工业技术—材料科学与工程]