电测与仪表2017，Vol.54Issue(21)：30-36,7.
基于卷积神经网络的绝缘子故障识别算法研究
Research of a faulted insulator identification algorithm based on convolution neural network
高强 1孟格格1
作者信息
- 1. 华北电力大学电气与电子工程学院,河北保定071003
- 折叠
摘要
Abstract
Convolution neural network has been widely used in image processing field.Meanwhile, different algo-rithms have different impact on network recognition rate.Based on this, we introduced wavelet decomposition theory and got that independent characteristics can express the original image more clearly,which was proved by BP propaga-tion algorithm and space vector theory.Because wavelet decomposition reduced the correlation between the Kernel and extracted more independent,comprehensive features with less convolution Kernel, thus the network performance was improved.Recognition experiments are conducted on MNIST,CIFAR-10 and CK standard database,the results show that the algorithm proposed in this paper can achieve higher recognition rate under the condition of different Kernel size and can obtain the recognition rate as the traditional algorithm with fewer iteration times and shorter training time. At last,this algorithm was applied in the fault identification of insulators and achieved good results.关键词
卷积神经网络/图像分类/核函数相关性/绝缘子/故障识别Key words
convolution neural network/image classification/the correlation between the Kernel/insulator/fault identification分类
信息技术与安全科学引用本文复制引用
高强,孟格格..基于卷积神经网络的绝缘子故障识别算法研究[J].电测与仪表,2017,54(21):30-36,7.
