PREDICTION OF NOx EMISSION BASED ON PRINCIPAL COMPONENTS AND BAYESIAN REGULARIZATION
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PREDICTION OF NOx EMISSION BASED ON PRINCIPAL COMPONENTS AND BAYESIAN REGULARIZATION
Thermal Power GenerationVol. 39, Issue 2, Pages: 24-27(2010)
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:
CLC:TK227.1
Published:2010
稿件说明:
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YANG Fei, LU Bao-lingCollege of Mechanical and Electronic Control Engineering. PREDICTION OF NOx EMISSION BASED ON PRINCIPAL COMPONENTS AND BAYESIAN REGULARIZATION[J]. Thermal power generation, 2010, 39(2): 24-27.
DOI:
YANG Fei, LU Bao-lingCollege of Mechanical and Electronic Control Engineering. PREDICTION OF NOx EMISSION BASED ON PRINCIPAL COMPONENTS AND BAYESIAN REGULARIZATION[J]. Thermal power generation, 2010, 39(2): 24-27.DOI:
PREDICTION OF NOx EMISSION BASED ON PRINCIPAL COMPONENTS AND BAYESIAN REGULARIZATION
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.