Application of RBF-BP neural network in the detection of nitrogen oxide concentration in thermal power plant

YAN Yue; YAN Shi; JIANG Yun

doi:10.13873/J.1000-9787(2016)12-0149-03

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Application of RBF-BP neural network in the detection of nitrogen oxide concentration in thermal power plant

更新时间：2025-09-11

- Application of RBF-BP neural network in the detection of nitrogen oxide concentration in thermal power plant
- Vol. 35, Issue 12, Pages: 149-151+160(2016)
- 作者机构：
  
  1. 重庆工商大学检测控制集成系统重庆市市级工程实验室
  2. 重庆川仪分析仪器有限公司
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2016)12-0149-03
  CLC： TM621;TP183
- Published：2016
- 稿件说明：
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[1]严玥,严实,江赟.RBF-BP网络在火电厂氮氧化物浓度检测的应用[J].传感器与微系统,2016,35(12):149-151+160.

YAN Yue, YAN Shi, JIANG Yun. Application of RBF-BP neural network in the detection of nitrogen oxide concentration in thermal power plant[J]. 2016, 35(12): 149-151+160.
[1]严玥,严实,江赟.RBF-BP网络在火电厂氮氧化物浓度检测的应用[J].传感器与微系统,2016,35(12):149-151+160. DOI： 10.13873/J.1000-9787(2016)12-0149-03.

YAN Yue, YAN Shi, JIANG Yun. Application of RBF-BP neural network in the detection of nitrogen oxide concentration in thermal power plant[J]. 2016, 35(12): 149-151+160. DOI： 10.13873/J.1000-9787(2016)12-0149-03.

摘要

在分析影响火电厂氮氧化物浓度检测精度的多种原因基础上

提出建立RBF-BP神经网络模型进行改进的方法

并详细说明了样本数据、神经网络构成、训练函数、回归因子、动量因子等关键技术。以2015年实际火电厂检测数据为依据

对建立的RBF-BP神经网络进行了仿真实验

并与单一RBF仿真实验进行对比分析

实验表明平均相对误差为0.3%

证明了该方法的有效性。

Abstract

Aiming at detection accuracy of nitrogen oxide concentration may interfered by all kinds of factors

the method of establishing the RBF-BP network is proposed

and then the sample data

Neural network structure

training function

regression factor

momentum factor are described. According from A Large thermal power plant’s real numbers in 2015

the computer simulation and analysis show that this method can effectively improve the accuracy. The overall-average deviation was 0. 3 %

1 %.

关键词

Keywords

references

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大数据分析的无限深度神经网络方法 [J]. 张蕾,章毅. 计算机研究与发展 . 2016(01)

基于RBF神经网络算法在重金属浓度测量中的应用 [J]. 李德霞,黄廷磊,林科,翟文军. 仪表技术与传感器 . 2015(10)

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