| 基于小波包分析与BP神经网络的氧化铝熟料检测 |
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| 引用本文： | 李斌,王恩成. 基于小波包分析与BP神经网络的氧化铝熟料检测[J]. 山东理工大学学报：自然科学版, 2013, 0(6): 40-43 |
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| 作者姓名： | 李斌 王恩成 |
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| 作者单位： | 北方工业大学信息工程学院,北京100041 |
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| 摘 要： | 利用小波包分析与BP(Back Propagation)神经网络相结合的算法,对氧化铝熟料检测的应用进行了研究.通过采集回转窑中氧化铝熟料下落碰撞窑壁产生的声音信号,利用小波包分析提取特征向量,根据氧化铝的烧结状况与声音信号特征向量的对应关系,提出建立BP神经网络模型.利用MATLAB对测试样本进行验证,结果表明BP神经网络模型在氧化铝熟料检测中具有可行性,而且具备一定的准确率.
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| 关 键 词： | 小波包分析 BP神经网络 氧化铝 熟料检测 |
The detection of alumina clinker based on wavelet packet analysis and BP network |
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| LI Bin,WANG En-cheng. The detection of alumina clinker based on wavelet packet analysis and BP network[J]. Journal of Shandong University of Technology:Science and Technology, 2013, 0(6): 40-43 |
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| Authors: | LI Bin WANG En-cheng |
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| Affiliation: | ( College of Information Engineering, North China University of Technology, Beijing 100041, China) |
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| Abstract: | This paper studies the application of wavelet packet analysis combined with BP neural network in alumina clinkers detection .The method includes gathering the sound signal produced by alumina clinkers collide the rotary kiln wall and extracting feature vectors using wavelet packet analysis from the sound signal .According to corresponding relationship between the alumina clinkers sintered condition and the sound signal feature vectors ,BP （Back Propagation ） neural network model is presented .MATLAB simulation results show that BP （Back Propagation） neural network model is a feasible plan in alumina clinkers detection with high accuracy rate . |
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| Keywords: | wavelet packet analysis BP neural network alumina clinker detection |
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