摘要
复杂光纤网络运行状态检测研究一直是人们关注的焦点,为了解决传统当前复杂光纤网络运行状态检测方法存在的一些缺陷,以提高复杂光纤网络运行状态检测精度为目标,提出了基于数据挖掘的复杂光纤网络运行状态检测方法,首先采集复杂光纤网络运行状态信号,提取可以描述复杂光纤网络运行状态的检测特征,然后引入极限学习机建立复杂光纤网络运行状态检测的分类器,最后在Matlab 2018平台与传统方法进行了复杂光纤网络运行状态检测对照实验。结果表明,本方法的复杂光纤网络运行状态检测精度超过92%,缩短了检测时间,复杂光纤网络运行状态检测结果明显优于传统方法。
The research of complex optical fiber network operation state detection has always been the focus of people’s attention. In order to solve some defects of the traditional detection method of complex optical fiber network operation state,in order to improve the detection accuracy of complex optical fiber network operation state,a complex optical fiber network operation state detection method based on data mining is proposed Signal extraction can describe the detection features of complex optical fiber network operation state,and then the extreme learning machine is introduced to establish the classifier of complex optical fiber network operation state detection. Finally,the complex optical fiber network operation state detection is verified with traditional methods on Matlab 2018 platform. The results show that the detection accuracy of complex optical fiber network is more than 92%,which shortens the detection time of complex optical fiber network,and the detection result of complex optical fiber network operation state is obviously better than the traditional method.
作者
闫俊辉
闫鑫
YAN Junhui;YAN Xin(Maths&Information Technology School,Yuncheng University,Yuncheng Shanxi 044000,China;School of Management,Shanghai University,Shanghai 200444,China)
出处
《激光杂志》
CAS
北大核心
2021年第9期94-97,共4页
Laser Journal
基金
国家自然科学基金(No.61821091)。
关键词
光纤通信技术
网络运行状态
检测方法
极限学习机
对照测试
检测精度
optical fiber communication technology
network operation state
detection method
extreme learning machine
contrast test
detection accuracy
