基于互信息和自组织RBF神经网络的出水BOD软测量方法

李文静; 李萌; 乔俊飞

doi:10.11949/j.issn.0438-1157.20181362

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基于互信息和自组织RBF神经网络的出水BOD软测量方法

过程系统工程 | 更新时间：2026-07-24

- 基于互信息和自组织RBF神经网络的出水BOD软测量方法
- Effluent BOD soft measurement based on mutual information and self-organizing RBF neural network
- 化工学报 2019年70卷第2期页码：687-695
- 作者机构：
  
  1.北京工业大学信息学部，北京 100124
  2.计算智能与智能系统北京市重点实验室，北京 100124
- 作者简介：
  
  李文静（1985—），女，博士，副教授，wenjing.li@bjut.edu.cn
- 基金信息：
  
  国家自然科学基金项目(61603009;61533002);北京市自然科学基金项目(4182007);北京市教委科技一般项目(KM201910005023);北京工业大学日新人才计划项目(2017-RX(1)-04)
- DOI：10.11949/j.issn.0438-1157.20181362
  中图分类号： TP 183
- 收稿：2018-11-18，
  
  修回：2018-11-22，
  
  网络首发：2020-06-16，
  
  纸质出版：2019-02-05
- 稿件说明：
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李文静, 李萌, 乔俊飞. 基于互信息和自组织RBF神经网络的出水BOD软测量方法[J]. 化工学报, 2019, 70(2): 687-695

LI Wenjing, LI Meng, QIAO Junfei. Effluent BOD soft measurement based on mutual information and self-organizing RBF neural network[J]. CIESC Journal, 2019, 70(2): 687-695
李文静, 李萌, 乔俊飞. 基于互信息和自组织RBF神经网络的出水BOD软测量方法[J]. 化工学报, 2019, 70(2): 687-695 DOI： 10.11949/j.issn.0438-1157.20181362.

LI Wenjing, LI Meng, QIAO Junfei. Effluent BOD soft measurement based on mutual information and self-organizing RBF neural network[J]. CIESC Journal, 2019, 70(2): 687-695 DOI： 10.11949/j.issn.0438-1157.20181362.

摘要

针对污水处理过程出水生化需氧量（biochemical oxygen demand，BOD）难以实时准确测量的问题，提出了一种基于互信息和自组织RBF神经网络的软测量方法对出水BOD进行预测。首先，使用基于互信息的方法提取相关特征参量作为软测量模型的输入变量；其次，设计一种基于误差校正-敏感度分析的自组织RBF神经网络，使用改进的Levenberg-Marquardt（LM）算法对网络进行训练以提高训练速度；最后将软测量模型应用于UCI公开数据集及实际的污水处理过程，实验结果表明该软测量模型结构紧凑，训练时间相对较短，预测精度有所提高，能够对出水BOD实现快速准确预测。

Abstract

It is difficult to achieve real-time accurate measurement for effluent biochemical oxygen demand (BOD). To solve this problem

a soft-measurement method based on mutual information and a self-organizing RBF neural network is proposed for BOD prediction in this paper. First

a method based on mutual information is employed to extract feature variables

and these variables are used as inputs to the soft-measurement model. Second

a self-organizing radial basis function (RBF) neural network based on error-correction method and sensitivity analysis is designed

and the improved Levenberg-Marquardt (LM) algorithm is used to train parameters of the neural network to shorten its training time. Finally

the soft-measurement model is applied to UCI public datasets and the real wastewater treatment process. The results show that the soft-measurement model has a more compact structure and relatively short training time

and improves the prediction accuracy

which realizes a fast and accurate prediction for BOD.

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Keywords

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