| 测定电位滴定终点的神经网络法 |
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| 引用本文： | 蔡煜东,吴伟,甘骏人,姚林声.测定电位滴定终点的神经网络法[J].分析化学,1993(4). |
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| 作者姓名： | 蔡煜东 吴伟 甘骏人 姚林声 |
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| 作者单位： | 中国科学院上海冶金研究所，中国科学院上海冶金研究所，中国科学院上海冶金研究所，中国科学院上海冶金研究所 上海 200050，上海 200050，上海 200050，上海 200050 |
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| 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。
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| 关 键 词： | 电位滴定终点 人工神经网络 反向传播模型二阶微商 |
Determination of Potential Titration End-point by Neural Network |
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| Cai Yudong,Wu Wei,Gan Junren,Yao Linsheng.Determination of Potential Titration End-point by Neural Network[J].Chinese Journal of Analytical Chemistry,1993(4). |
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| Authors: | Cai Yudong Wu Wei Gan Junren Yao Linsheng |
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| Abstract: | 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. |
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| Keywords: | Potential titration end-point Artificial neural network Improved back-propagation model Second order derivative |
| 本文献已被 CNKI 等数据库收录！ |
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