径向基神经网络微分脉冲溶出伏安法同时测定铅、镍和镉

Simultaneous Determination of Pb(Ⅱ),Ni(Ⅱ)and Cd(Ⅱ)by Differential Pulse Stripping Voltammetry with Radial Basis Function Neural Networks

径向基函数神经网络 (RBFN)用于分析重叠的伏安信号 ;一个名为径向基神经网络的程序 (PRBFN)被设计用于执行有关计算 ;RBFN法成功地应用于微分脉冲阳极溶出伏安法同时测定Pb(Ⅱ) ,Ni(Ⅱ)和Cd(Ⅱ) ;RBFN和偏最小二乘 (PLS)法的所有组分相对预测标准偏差 (RSEP)分别为4.8 %和5.6 % ;试验结果表明RBFN法是解决局部最小和改善收敛速率的一种有价值的工具。

Radial basis function networks were applied to analyze overlapping voltammetric signals.A proˉgramcalled PRBFNwas designed to performrelative calculations.The RBFNhas been showed to be a sucˉcessful approach for simultaneous determination of Pb(Ⅱ),Ni(Ⅱ)and Cd(Ⅱ)by means of differential pulse stripping voltammetry.The relative standard derivations of prediction(RSEP)for all components with RBFN and PLS methods were4.8%and5.6%,respectively.Experimental results showed that the RBFN was a valuable tool in solving the local minimum problem and improving the convergence rate.