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科技导报
|研究论文
2012
, 30
(23) :
46
-50
基于BP神经网络的垃圾热值预测模型
全屏
马晓茜, 谢泽琼
作者信息
Prediction Models for the Heating Values of Municipal Refuse Based on BP Neural Network
MA Xiaoqian, XIE Zeqiong
Affiliations
School of Electric Power, South China University of Technology, Guangzhou 510640, China
出版时间: 2012-08-18
doi: 10.3981/j.issn.1000-7857.2012.23.006
文章导航
入炉垃圾热值不稳定,对焚烧炉的稳定运行有很大影响。采用遗传算法优化BP神经网络的权值和阈值,建立垃圾焚烧炉入炉垃圾热值的预测模型。利用Garson方法和主成分分析法对某垃圾焚烧电厂在线运行数据进行分析后,作为BP神经网络的输入参数,实现入炉垃圾热值的在线测量和预测。研究结果表明,该模型预测平均相对误差为2.64%,检验样本相对误差平均值概率为95%的置信区间为[-1.75,2.59],有较高的准确性和置信度,具有较好的工程应用价值。
主成分分析法
/
遗传算法
/
BP神经网络
/
垃圾焚烧炉
/
热值
The heating values of municipal refuse entering into the incinerator are unstable, which have a great influence on the stable operation of the incineration. By using genetic algorithms to optimize initial weighs and thresholds of BP neural network, a predictive model is established to predict the heating values of municipal refuse. The online operating data are processed based on Garson method and principal component analysis, and then those data are used as input parameters of BP neural network. The measurement and prediction of the heating values of municipal refuse are able to be obtained on-line. The results show that the relative average error for the predicted values is 2.64%. The confidence interval (95%) of the relative average error for the test samples is between -1.75 and 2.59. Therefore, the model has both high accuracy and high confidence level, which is very suitable for engineering applications.
principal component analysis
/
genetic algorithms
/
BP neural network
/
incinerator
/
heating value
马晓茜;谢泽琼.
基于BP神经网络的垃圾热值预测模型.
科技导报,
2012
, 30
(23)
: 46
-50
.
DOI: 10.3981/j.issn.1000-7857.2012.23.006
MA Xiaoqian;XIE Zeqiong.
Prediction Models for the Heating Values of Municipal Refuse Based on BP Neural Network[J].
Science & Technology Review ,
2012
, 30
(23)
: 46
-50
.
DOI: 10.3981/j.issn.1000-7857.2012.23.006
2012年第30卷第23期
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文章信息
doi: 10.3981/j.issn.1000-7857.2012.23.006
接收时间：2011-12-19
首发时间：2012-08-18
出版时间：2012-08-18
收稿日期：2011-12-19
修回日期：2012-07-04
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2012.23.006
引用本文
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2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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