Research on prediction model of residual chlorine concentration based on RBF neural network

XIE Xin; GUO Peng-fei; ZHAN Xiao-li

doi:10.13873/j.1000-97872012.08.010

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Research on prediction model of residual chlorine concentration based on RBF neural network

更新时间：2025-09-11

- Research on prediction model of residual chlorine concentration based on RBF neural network
- Vol. 31, Issue 8, Pages: 64-65+68(2012)
- 作者机构：
  
  华东交通大学信息工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872012.08.010
  CLC： TU991;TP183
- Published：2012
- 稿件说明：
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[1]谢昕,郭鹏飞,詹小丽.基于RBF神经网络的余氯浓度预测模型研究[J].传感器与微系统,2012,31(08):64-65+68.

XIE Xin, GUO Peng-fei, ZHAN Xiao-li. Research on prediction model of residual chlorine concentration based on RBF neural network[J]. 2012, 31(8): 64-65+68.
[1]谢昕,郭鹏飞,詹小丽.基于RBF神经网络的余氯浓度预测模型研究[J].传感器与微系统,2012,31(08):64-65+68. DOI： 10.13873/j.1000-97872012.08.010.

XIE Xin, GUO Peng-fei, ZHAN Xiao-li. Research on prediction model of residual chlorine concentration based on RBF neural network[J]. 2012, 31(8): 64-65+68. DOI： 10.13873/j.1000-97872012.08.010.

摘要

余氯浓度是衡量供水管网水质的一个重要指标

采用混沌理论、模型校正等传统方法不能准确反映余氯浓度变化规律。根据RBF神经网络快速收敛和全局优化的特点

基于时间序列法

建立RBF神经网络余氯浓度预测模型。采用Matlab中的Newrbe函数进行函数逼近

结合某管网水质模拟控制系统提供的样本数据进行仿真计算

最终获得的余氯浓度预测值和实测值十分吻合。结果表明:RBF神经网络预测模型具有一定的工程实用价值。

Abstract

The concentration of residual chlorine is an important parameter indicating the quality of water in water distribution network

but the traditional methods

such as chaos theory and model calibration

cannot reflect the variation rule of residual chlorine thoroughly.A prediction model of residual chlorine is established

based on the fast convergence and global optimization of RBF neural network and time series.The Newrbe function in Matlab is adopted to realize function approximation

combining with the sample data provided by the water quality control system of water distribution network to run simulation.The difference between predicted residual chlorine fit actual ones

which means this model has practical value.

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references

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基于GA优选参数的RBF神经网络水质评价 [J]. 何同弟,李见为,黄鸿. 计算机工程 . 2011(11)

几种人工神经网络法的水质预测比较 [J]. 陈默,李书博,王硕. 甘肃水利水电技术 . 2011(03)

Evaluation of the Occurrence Possibility of SNP in Brassica napus with Sliding Window Features by Using RBF Networks [J]. HU Xuehai1,LI Ruiyuan2,MENG Jinling2,XIONG Huijuan1,XIA Jingbo1,LI Zhi1 1. College of Science,Huazhong Agricultural University,Wuhan 430070,Hubei,China; 2. National Key Laboratory of Crop Genetic Improvement,Huazhong Agricultural University,Wuhan 430070,Hubei,China. Wuhan University Journal of Natural Sciences . 2011(01)

基于AGA-RBF神经网络的电厂水质评价 [J]. 赵国材,佟维妍. 微计算机信息 . 2010(25)

基于GA优选参数的SVM水质评价方法研究 [J]. 周兆永,汪西莉,曹艳龙. 计算机工程与应用 . 2008(04)

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