(1. 上海電力學(xué)院 數(shù)理學(xué)院，上海 201399;
2. 上海大學(xué) 計算機工程與科學(xué)學(xué)院，上海 200444)
摘 要: 根據(jù)一系列 Al基非晶合金薄帶實測數(shù)據(jù)集，應(yīng)用粒子群優(yōu)化支持向量回歸方法(PSO-SVR)，建立一個通過相關(guān)表征參數(shù)來預(yù)測Al基非晶合金晶化溫度(Tx)的模型。利用該模型對不同類型鋁基非晶合金的晶化溫度(Tx)進行建模和預(yù)測研究，并與反向傳播神經(jīng)網(wǎng)絡(luò)(BPNN)預(yù)測方法進行比較。結(jié)果表明:基于留一交叉驗證法 (LOOCV)的PSO-SVR模型預(yù)測的晶化溫度誤差要比BPNN模型預(yù)測的小得多，這說明模型中所采用的特征參數(shù)能很好地描述該系列Al基非晶合金的晶化行為和熱穩(wěn)定性。
關(guān)鍵字: Al基非晶合金;晶化溫度;支持向量回歸;粒子群優(yōu)化
（1. School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 201399, China;
2. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China）
Abstract: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.
Key words: Al-based amorphous alloy; crystallization temperature; support vector regression; particle swarm optimization
