测定电位滴定终点的神经网络法  

Determination of Potential Titration End-point by Neural Network

作　　者：蔡煜东[1] 吴伟[1] 甘骏人[1] 姚林声 

机构地区：[1]中国科学院上海冶金研究所,上海200050

出　　处：《分析化学》1993年第4期439-442,共4页Chinese Journal of Analytical Chemistry

摘　　要：本文利用改进的“反向传播”神经网络模型,在滴定突跃附近,建立了E-V曲线的神经网络插值模型,由其二阶微商求得滴定终点。计算实例中,拟合最大相对误差不超过0.1％,计算机CPU时间不超过20s,实验结果表明,该方法性能良好,在电容量分析方面有广阔的应用前景。The approximation model for E-Vcurve near titration steep is constructed by an improved back-propagation neural network model. By calculating the second order derivative of the constructed model, titration end-point is determined. In our examples, the maximum fitting relative error does not exceed 0.1% and the total calculating time does not exceed 20 s. The experimental results show that the neural network is good,and it might be widely used in electro-volumetric analysis.

关 键 词：电位滴定终点 人工 神经网络 

分 类 号：O655.2[理学—分析化学]