电测与仪表2018，Vol.55Issue(5)：46-50,5.
基于多层卷积神经网络的变电站异常场景识别算法
A substation abnormal scene recognition algorithm based on multi-layer convolution neural network
孟格格 1高强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
Aiming at the problem of low recognition rate of small samples by convolution neural network,the concept of confidence is introduced,and a new image classification method based on multi-layer convolution neural network is proposed,which is called M_CNN,and its application in substation abnormal scene recognition is put forward.the confidence decision function is set according to the network identification of the small sample,and the samples are picked out for which are difficult to identify in the trained single-layer network,re-extracted features and trained the next layer of the network,forming the multilayer convolution neural network structure,so as to achieve the aim of improving recognition performance.The identification results on MNIST database with different sample sizes demonstrate that M_CNN model has some superiority in identifying small samples.At last,the M_CNN model is applied in substation abnormal scene recognition and achieves pretty results.关键词
置信度/多层卷积神经网络/小样本/变电站/异常场景识别Key words
confidence/multi-layer convolution neural network/small samples/substation/recognition of abnormal scene分类
信息技术与安全科学引用本文复制引用
孟格格,高强..基于多层卷积神经网络的变电站异常场景识别算法[J].电测与仪表,2018,55(5):46-50,5.
