【摘要】 为了研究AB5型储氢合金初始放电容量与合金成分间的关系,设计了径向基函数型人工神经网络模型。用"留一法"训练了模型,然后用训练好的神经网络模型预测了5个样本的初始放电容量,预测值和实验值在散点图中沿45°线分布,统计学指标为:均方误差(MSE)为6.063,相对均方误差(MSRE)为0.0262%,拟合分值(VOF)为1.934 5,说明人工神经网络预测的结果是准确、可靠的。最后用神经网络分析了AB5型储氢合金的合金成分对其初始放电容量的定量影响,结果表明:La、Nd含量对初始放电容量影响呈抛物线关系,初始放电容量存在一个极小值;Ce含量对初始放电容量影响较大,随Ce含量的增加而增大,且增幅较大;Pr含量的影响不大,随Pr含量的增加初始放电容量有小幅增大,最后趋于平稳。更多还原

【Abstract】 Radial-basis function artificial neural network(ANN)was developed to study the quantitative relationship between the alloying component and initial discharge capacity of AB5-based hydrogen storage alloy."Leave-one out"method was used to train the ANN model.After being trained,ANN model was used to predict the initial discharge capacity of five samples,the prediction and the measured values distribute along the 45°diagonal line in the scatter diagram,statistical indicators were MSE=6.063,MRSE=0.0262%,VOF=1.9345,which showed that the prediction results of ANN model were accurate and reliable.The quantitative effects of alloying component on the initial discharge capacity were analyzed by ANN model.The results show that parabola relationship exists between the initial discharge capacity and La and Nd content.Ce content has a great effect on the initial discharge capacity which increases with the increase of Ce content.With the increase of Pr content,the initial discharge capacity increases slightly and leveled off finally.更多还原