Solar panels defect detection based on radial basis function neural network

	Solar panels defect detection based on radial basis function neural network
	Shen, L.-Y.; M. Zhu and X.-Y. Chen
	2015
发表期刊	Faguang Xuebao/Chinese Journal of Luminescence
卷号	36 期号:1 页码:99-105
摘要	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.
文章类型	期刊论文
收录类别	EI
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/55986
专题	中科院长春光机所知识产出
推荐引用方式 GB/T 7714	Shen, L.-Y.,M. Zhu and X.-Y. Chen. Solar panels defect detection based on radial basis function neural network[J]. Faguang Xuebao/Chinese Journal of Luminescence,2015,36(1):99-105.
APA	Shen, L.-Y.,&M. Zhu and X.-Y. Chen.(2015).Solar panels defect detection based on radial basis function neural network.Faguang Xuebao/Chinese Journal of Luminescence,36(1),99-105.
MLA	Shen, L.-Y.,et al."Solar panels defect detection based on radial basis function neural network".Faguang Xuebao/Chinese Journal of Luminescence 36.1(2015):99-105.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
基于径向基神经网络的太阳能电池缺陷检测.（459KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	浏览下载