| 基于主成分和贝叶斯正则化的NO_x排放量的预测 |
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| 引用本文： | 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). DOI: 10.3969/j.issn.1002-3364.2010.02.024 |
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| 作者姓名： | 杨飞 卢保玲 |
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| 作者单位： | 北京交通大学机械与电子控制工程学院,北京,100044 |
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| 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。
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| 关 键 词： | 300 MW机组 锅炉 NOx排放量 主成分分析 BP神经网络 贝叶斯正则化 |
PREDICTION OF NO_x EMISSION BASED ON PRINCIPAL COMPONENTS AND BAYESIAN REGULARIZATION |
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| YANG Fei,LU Bao-ling. PREDICTION OF NO_x EMISSION BASED ON PRINCIPAL COMPONENTS AND BAYESIAN REGULARIZATION[J]. Thermal Power Generation, 2010, 39(2). DOI: 10.3969/j.issn.1002-3364.2010.02.024 |
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| Authors: | YANG Fei LU Bao-ling |
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| Affiliation: | YANG Fei,LU Bao-lingCollege of Mechanical , Electronic Control Engineering,Beijing Jiaotong University,Beijing 100044,PRC |
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| 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... |
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| Keywords: | 300 MW unit boiler NOx emission principal component analysis BP neural network Bayesian regularization |
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