基于主成分和贝叶斯正则化的NO<sub>x</sub>排放量的预测

杨飞; 卢保玲

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基于主成分和贝叶斯正则化的NO_x排放量的预测

热能基础研究 | 更新时间：2025-09-28

- 基于主成分和贝叶斯正则化的NO_x排放量的预测
- PREDICTION OF NO_x EMISSION BASED ON PRINCIPAL COMPONENTS AND BAYESIAN REGULARIZATION
- 热力发电 2010年39卷第2期页码：24-27
- 作者机构：
  
  北京交通大学机械与电子控制工程学院
- 作者简介：
- 基金信息：
- DOI：
  中图分类号： TK227.1
- 纸质出版：2010
- 稿件说明：
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杨飞, 卢保玲. 基于主成分和贝叶斯正则化的NO_x排放量的预测[J]. 热力发电, 2010,39(2):24-27.

YANG Fei, LU Bao-lingCollege of Mechanical and Electronic Control Engineering. PREDICTION OF NO_x EMISSION BASED ON PRINCIPAL COMPONENTS AND BAYESIAN REGULARIZATION[J]. Thermal power generation, 2010, 39(2): 24-27.
杨飞, 卢保玲. 基于主成分和贝叶斯正则化的NO_x排放量的预测[J]. 热力发电, 2010,39(2):24-27. DOI：

YANG Fei, LU Bao-lingCollege of Mechanical and Electronic Control Engineering. PREDICTION OF NO_x EMISSION BASED ON PRINCIPAL COMPONENTS AND BAYESIAN REGULARIZATION[J]. Thermal power generation, 2010, 39(2): 24-27. DOI：

摘要

针对电厂燃煤锅炉NOx排放量预测建模中输入因子过多而导致神经网络结构规模过大、泛化能力差的问题

通过主成分分析和贝叶斯正则化的方法对BP神经网络进行改进

优化网络结构

从而提高了泛化能力。以某300 MW机组锅炉热态多工况试验数据为例

改进的神经网络预测方法与传统的神经网络方法相比

泛化能力有显著提高

而且网络的收敛稳定

实际预测效果良好。

Abstract

Directing against the problems of too large size and poor generalization capability of the neural metwork structure due to too many input factors in establishing model for predicting NOx emission from coal-fired boilers in power plants

the BP neural network has been modified through principal component analysis and Bayesian regularization

the network structure being optimized

thereby

enhancing its generalization capability.Taking test data of boiler for one 300 MW unit under many operating conditions of hot state as example

through comparison of the prediction method using modified neural network with the method of traditional nuural network

the generalization capability has been remakably enhanced

and the covergence of network being stable

the effectiveness of practical prediction being good.

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基于主成分和贝叶斯正则化的NOx排放量的预测

PREDICTION OF NOx EMISSION BASED ON PRINCIPAL COMPONENTS AND BAYESIAN REGULARIZATION

DOI：

摘要

Abstract

关键词

Keywords

references

基于主成分和贝叶斯正则化的NO_x排放量的预测

PREDICTION OF NO_x EMISSION BASED ON PRINCIPAL COMPONENTS AND BAYESIAN REGULARIZATION