电测与仪表2017，Vol.54Issue(23)：54-59,6.
深度信念网络的等效模型及权值扩展算法研究
Research on equivalent model and weight extension algorithm of deep belief network
高强 1王明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 accuracy of the training model in the case of less data samples in deep belief network (DBN),which led to the classification recognition rate is not ideal,so the system performance needs to be improved.This paper researches the equivalent model of DBN,analyzes the problem of poor recognition rate in the case of small samples;Then,a weight expansion algorithm is proposed to enlarge the matching space between the sample and weight,so that the decision is more conducive to correct classification,which improves the accuracy of image classification under the condition of small sample size;The algorithm is proved that can promote the performance of the system by using the detection and estimation theory,the test of different sample banks is further proof of this completion.Finally,the method is applied to small sample faulted insulator recognition.关键词
深度信念网络/等效模型/最佳接收/小样本/区间数Key words
deep belief network/equivalent model/best reception/small sample/interval number分类
信息技术与安全科学引用本文复制引用
高强,王明..深度信念网络的等效模型及权值扩展算法研究[J].电测与仪表,2017,54(23):54-59,6.
