| 基于遗传算法优化的煤粉着火温度BP神经网络预测模型 |
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| 引用本文： | 杨建国,赵虹,岑可法. 基于遗传算法优化的煤粉着火温度BP神经网络预测模型[J]. 煤炭学报, 2006, 31(2): 211-214 |
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| 作者姓名： | 杨建国 赵虹 岑可法 |
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| 作者单位： | 浙江大学 热能工程研究所，能源洁净利用与环境工程教育部重点实验室，浙江 杭州，310027 |
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| 摘 要： | 采用遗传算法（GA）对BP神经网络（结构和初始权值、阈值）进行了优化，获得了影响煤粉着火温度预测的主要煤质指标（Mad，Aad，Vad，Oad），建立了优化的煤粉着火温度BP神经网络预测模型．对20个校验样本的预测结果表明：预测值与试验值的平均相对误差为0.29%，均方差为59.29，达到了较高的预测精度．
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| 关 键 词： | 煤粉 着火温度 遗传算法 神经网络 预测模型 |
| 文章编号： | 0253-9993(2006)02-0211-04 |
| 收稿时间： | 2005-09-28 |
| 修稿时间： | 2005-09-28 |
Optimized BP network model for predicting igniting temperature of pulverized-coal based on genetic algorithm |
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| YANG Jian-guo,ZHAO Hong,CEN Ke-fa. Optimized BP network model for predicting igniting temperature of pulverized-coal based on genetic algorithm[J]. Journal of China Coal Society, 2006, 31(2): 211-214 |
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| Authors: | YANG Jian-guo ZHAO Hong CEN Ke-fa |
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| Affiliation: | Clean Energy and Environment Engineering Key Lab of Ministry of Education, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China |
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| Abstract: | The BP network optimized with GA(genetic algorithm)was built for prediction of igniting temperature of coal.According to the result,the main quality parameters of coal which influence igniting temperature mostly were obtained.And the optimized BP network model for predicting igniting temperature of coal was built.Tested with 20 samples,the mean relative error of prediction is 0.29%,and the mean square error is 59.29.The optimized BP network model has perfect precision. |
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| Keywords: | pulverized-coal igniting temperature genetic algorithm BP network prediction model |
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