| 盐湖水化学类型的人工神经网络判别方法 |
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| 引用本文： | 吴启勋,李磊,安燕. 盐湖水化学类型的人工神经网络判别方法[J]. 分析科学学报, 2005, 21(3): 271-273 |
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| 作者姓名： | 吴启勋 李磊 安燕 |
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| 作者单位： | 青海民族学院化学系,西宁,810007 |
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| 基金项目： | 教育部科学技术研究项目 |
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| 摘 要： | 研究了作为典型径向基函数网络之一的概率神经网络在盐湖水化学类型分类预测中的应用，验证了该方法的可靠性，得到了满意的分类预测结果。实验结果和网络结构分析表明，概率神经网络方法比熟知的反向传播算法(BP)网络要好。概率神经网络的研究应用为化学模式识别提供了一个新工具。
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| 关 键 词： | 盐湖 水化学类型 人工神经网络 径向基函数网络 概率神经网络 |
| 文章编号： | 1006-6144(2005)03-0271-03 |
| 修稿时间： | 2003-10-08 |
Artificial Neural Network (ANN) Method for Predicting of Hydrochemical Types of Salt Lakes |
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| WU Qi-Xun,LI Lei,An Yan. Artificial Neural Network (ANN) Method for Predicting of Hydrochemical Types of Salt Lakes[J]. Journal of Analytical Science, 2005, 21(3): 271-273 |
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| Authors: | WU Qi-Xun LI Lei An Yan |
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| Abstract: | The prediction of hydrochemical types of salt lakes by probability artificial neural network model, which is one of the typical Radial basis function networks was studied. The good classing and predicting results were obtained. The average accuracy for predicting of hydrochemical types of salt lakes was 91.0%. Both the experimental results and structure analysis of neural networks indicated that the probability artificial neural network method is much better than the back-propagation (BP) neural network method . In fact, this study provides a new tool for chemical pattern recognition. |
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| Keywords: | Salt lake Hydrochemical types Artificial neural network (ANN) Radial basis function network(RBFN) Probability artificial neural network(PANN) |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
