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1. 中国科学院 长春光学精密机械与物理研究所,吉林 长春,中国,130033
2. 中国科学院大学 北京,100049
3. 长春理工大学 电子信息工程学院,吉林 长春,中国,130022
纸质出版日期:2015-1-3,
网络出版日期:2014-11-18,
收稿日期:2014-8-22,
修回日期:2014-10-19,
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沈凌云, 朱明, 陈小云. 基于径向基神经网络的太阳能电池缺陷检测[J]. 发光学报, 2015,36(1): 99-105
SHEN Ling-yun, ZHU Ming, CHEN Xiao-yun. Solar Panels Defect Detection Based on Radial Basis Function Neural Network[J]. Chinese Journal of Luminescence, 2015,36(1): 99-105
沈凌云, 朱明, 陈小云. 基于径向基神经网络的太阳能电池缺陷检测[J]. 发光学报, 2015,36(1): 99-105 DOI: 10.3788/fgxb20153601.0099.
SHEN Ling-yun, ZHU Ming, CHEN Xiao-yun. Solar Panels Defect Detection Based on Radial Basis Function Neural Network[J]. Chinese Journal of Luminescence, 2015,36(1): 99-105 DOI: 10.3788/fgxb20153601.0099.
为了检测太阳能电池的缺陷
建立了太阳能电池板的电致发光(EL)图像与其缺陷类型间的神经网络预测模型
可以对太阳能电池板不同类型缺陷进行自适应检测。首先
采用主成分分量分析(PCA)算法对电致发光(EL)图像训练样本集降维;然后
将降维后得到的数据输入神经网络预测模型进行学习
对模型的参数进行优化选取;最后
将训练好的网络对测试样本集进行仿真。仿真结果表明:在采用相同的训练样本集和测试样本集条件下
与反向传播神经网络(BPNN)相比
径向基神经网络(RBFNN)具有全局最优特性
结构简单
最高识别率达96.25%
计算时间较短
能满足在线检测的要求。
In order to detect the defect on solar panels and improve the conversion efficiency
two neural network models were established between solar panels electroluminescence (EL) images and defect types
which can detect different types of defects on solar panels adaptively. Firstly
the dimensions of EL images training samples set were reduced by using principal component analysis (PCA). Then
EL images training samples set after dimension reduction was put into the neural networks for training. Finally
the testing samples set was simulated by the trained network through choosing the best parameters. Compared with BPNN
RBFNN has the advantages of global optimization characteristics and simple structure
which leads to the highest accuracy rate of 96.25% and shorter computational time. The experiment results show that RBFNN can meet the requirements of online detection.
缺陷检测反向传播神经网络径向基神经网络主成分分析降维
defect detectionback propagation neural network (BPNN)radial basis function neural network (RBFNN)principal component analysis (PCA)dimension reduction
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Jin J, Zhang Z G, Wang Z, et al. Temperature errors compensation for digital closed-loop fiber optic gyroscope using RBF neural networks [J]. Opt. Precision Eng.(光学 精密工程), 2008, 16(2):235-240 (in Chinese).
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Deng X L, Cheng K, Wu W B. Detection of citrus huanglongbing based on principal component analysis and back propagation neural network [J]. Acta Photon. Sinica (光子学报), 2014, 43(4):1-7 (in Chinese).
Xu Z M, Zhou J Z, Huang S, et al. Quality prediction of laser milling based on optimized back propagation networks by genetic algorithms [J]. Chin. J. Lasers (中国激光), 2013, 40(6):1-8 (in Chinese).
Alexandridis A, Chondrodima E. A medical diagnostic tool based on radial basis function classifiers and evolutionary simulated annealing [J]. J. Biomed. Inform., 2014, 49(1):61-72
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Ghosh-Dastidar S, Adeli H, Dadmehr N. Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection [J]. IEEE Trans. Biomed. Eng., 2008, 55(2):512-518.
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Li G N, Tan Q C, Zhang K, et al. Solar cells defect detection in electroluminescence images [J]. Chin. J. Lumin.(发光学报), 2013, 34(10):1400-1407 (in Chinese).
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