Application of Hopfield Neural Network Based on Factor Analysis to Water Quality Evaluation

LU Wen-xi; CHU Hai-bo; WANG Xi-hua; GONG Lei

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Application of Hopfield Neural Network Based on Factor Analysis to Water Quality Evaluation

- Application of Hopfield Neural Network Based on Factor Analysis to Water Quality Evaluation
- Bulletin of Soiland Water Conservation Vol. 31, Issue 1, Pages: 197-200(2012)
- 作者机构：
  
  吉林大学环境与资源学院,吉林,长春,130021
- 作者简介：
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- DOI：
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- Published：2012
- 稿件说明：
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LU Wen-xi, CHU Hai-bo, WANG Xi-hua, et al. Application of Hopfield Neural Network Based on Factor Analysis to Water Quality Evaluation[J]. Bulletin of Soiland Water Conservation, 2012, 31(1): 197-200.
DOI：

LU Wen-xi, CHU Hai-bo, WANG Xi-hua, et al. Application of Hopfield Neural Network Based on Factor Analysis to Water Quality Evaluation[J]. Bulletin of Soiland Water Conservation, 2012, 31(1): 197-200. DOI：

摘要

针对Hopfield神经网络的过度拟合问题

在因子分析的基础上

结合Hopfield神经网络模型提出了因子分析—Hopfield神经网络模型。以东辽河为例

采用因子分析法确定7个水质评价因子

再建立5×7的Hopfield神经网络进行水质综合评价

并与单一的Hopfield网络和传统的内梅罗指数法的结果进行了比较。结果表明

因子分析—Hopfield神经网络明显优于单一的Hopfield神经网络

不仅在一定程度上弥补了因子分析在实际应用中没有实现水质分级的缺陷

而且有效地降低了Hopfield神经网络的过度拟和的程度

评价结果更为科学合理

为水质综合评价提供了一种新的方法

具有极好的应用前景。

Abstract

To solve the over-fitting problem of Hopfield neural network

Hopfield neural network model with factor analysis was proposed based on factor analysis combined with Hopfield neural network model.Taking Dongliao River for an example

the model determined seven water quality evaluation factors using factor analysis method

created a 5×7 Hopfield neural network to evaluate water quality comprehensively

and compared the results from single Hopfield neural network and traditional Nemero Index method.Results showed that the factor analysis Hopfield neural network is much better than single Hopfield neural network.It not only makes up for the defect that factor analysis does not achieve the classification of water quality in practical applications

but also effectively reduces the extent of over-fitting of Hopfield neural network.The evaluation results are more reasonable and the model provides a new approach to comprehensive water quality evaluation with some excellent prospects.

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