基于聚类分析提取特征的光通信系统异常数据检测
Detection of abnormal data in optical communication system based on clustering analysis and feature extraction
刘永立 1翟伟芳 1冯娟1
作者信息
- 1. 保定理工学院信息科与工程学院,河北保定 071000
- 折叠
摘要
光通信系统易受多种因素影响,传统方法的光通信系统异常数据检测错误率高,检测效率,为了获得理想的光通信系统异常数据检测结果,设计了基于聚类分析提取特征的光通信系统异常数据检测方法.首先设计光通信系统数据传输模型,采用聚类算法提取光通信系统异常数据特征,然后采用深度学习算法建立光通信系统异常数据检测模型,并采用遗传算法优化深度学习算法,最后进行了光通信系统异常数据检测仿真实验,结果表明:本方法的光通信系统异常数据检测正确率超过98％,光通信系统异常数据检测时间为21.6 ms,具有一定的实际应用价值.
Abstract
Optical communication systems are susceptible to various factors.Traditional methods for detecting ab-normal data in optical communication systems have high error rates and detection efficiency.In order to obtain ideal abnormal data detection results in optical communication systems,a clustering analysis based feature extraction method for abnormal data detection in optical communication systems was designed.Firstly,a data transmission model for opti-cal communication systems is designed,and clustering algorithms are used to extract abnormal data features.Then,a degree learning algorithm is used to establish an abnormal data detection model for optical communication systems,and genetic algorithms are used to optimize deep learning algorithms.Finally,a simulation experiment for abnormal data detection in optical communication systems is conducted,and the results show that the accuracy of the proposed meth-od for detecting abnormal data in optical communication systems exceeds 98％,The detection time of abnormal data in the optical communication system is 21.6 ms,which has certain practical application value.
关键词
聚类分析算法/光通信系统/异常数据/检测模型Key words
cluster analysis algorithm/optical communication system/abnormal data/detection model引用本文复制引用
基金项目
河北省省级科技计划软科学研究专项(21555401D)
全国高等院校计算机基础教育教学研究项目(2022-AFCEC-178)
出版年
2024
NETL
NSTL
万方数据