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《Chinese Journal of Luminescence》 2015-01
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Solar Panels Defect Detection Based on Radial Basis Function Neural Network

SHEN Ling-yun;ZHU Ming;CHEN Xiao-yun;Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences;University of Chinese Academy of Sciences;School of Electronics and Information Engineering,Changchun University of Science and Technology;  
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.
【Fund】: 国家自然科学基金(61203242);; 中国科学院二期创新工程基金(C50T0P2)资助项目
【CateGory Index】: TP183;TM914.4
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