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作 者:王向中[1] 查五生[1] 刘锦云[1] 储林华[1]
机构地区:[1]西华大学材料科学与工程学院,四川成都610039
出 处:《电子元件与材料》2009年第8期75-77,85,共4页Electronic Components And Materials
基 金:四川省教育厅重点资助项目(No.2004A110)
摘 要:针对一般BP神经网络泛化能力差,在Bayesian正则化BP神经网络的基础上,运用加权检验、"表决网"等方法的思路训练网络,并通过主成分分析方法对输入数据进行降维,建立了磁粉制备工艺(淬速度和晶化退火温度)、合金成分与磁性能之间的BPNN(back propagation network)预测模型。结果表明:该模型泛化能力较高,预测的Br相对误差在2%左右、Hcj和(BH)max都在5%以内,且每次预测的相对误差平均值波动不超过1%。The (Nd2Fe14B/α-Fe) permanent magnetic property prediction model was bulit by taking magnetic particle preparation processes(spinning speed and annealing temperature) and alloy components as network input, the magnetic properties as output. For enhancing the model's ability of generalization it was trained by the way of weighted detecting method and clustering multiple based on the Bayesian-regularization BP neural network. The input data was analyzed the principal components for reducing its dimension.The results show that this model's generalization is better. The relative error between the measured value and predicted value of Br is confined to about 2% and that of Hcj, (BH)max to 5%. And the average of the relative error fluctuates within 1% in every prediction.
关 键 词:纳米晶复相(Nd2Fe14B/α-Fe)永磁体 主成分分析 BAYESIAN 正则化BP神经网络 泛化
分 类 号:TP183[自动化与计算机技术—控制理论与控制工程]
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