基于智能计算的网络偏差数据提取数学仿真  

Mathematical Simulation of Network Deviation Data Extraction Based on Intelligent Computing

作　　者：陈艳君[1] 陈婷婷[1] CHEN Yan-jun;CHEN Ting-ting(College of Science and Technology,Nanchang University,Nanchangjiangxi 330029,China)

机构地区：[1]南昌大学科学技术学院,江西南昌330029

出　　处：《计算机仿真》2021年第8期364-367,406,共5页Computer Simulation

摘　　要：针对现有数据提取方法未考虑空间高复杂度,导致偏差数据检测的缺失、提取覆盖率较低、时间消耗较多的问题,提出基于智能计算的网络偏差数据提取数学仿真方法。引用信息熵检测网络偏差数据,并采用遗传算法对上述检测结果更新与平滑处理,获取网络偏差数据特征响应函数,结合距离与密度参数,确定网络偏差数据的位置与密度,最终实现网络偏差数据的快速提取。数学仿真结果显示,在网络吞吐量10000-50000Mbps背景下,与现有方法相比较,提出方法的偏差数据提取覆盖率更大,时间消耗更少,充分证实了提出方法具备更好提取效果。The existing data extraction methods do not consider the high spatial complexity,which leads to the lack of deviation data detection,low extraction coverage and more time consumption.A mathematical simulation method of network deviation data extraction based on intelligent computing is proposed.The information entropy was used to detect the network deviation data,and the genetic algorithm was used to update and smooth the above detection results to obtain the characteristic response function of the network deviation data.Combined with the distance and density parameters,the position and density of the network deviation data were determined,and finally the fast extraction of the network deviation data was realized.The simulation results show that under the background of network throughput of 10000-50000 mbps,compared with the existing methods,the proposed method has larger coverage of deviation data extraction and less time consumption,which fully proves that the proposed method has better extraction effect.

关 键 词：智能计算 网络偏差数据 提取覆盖率 信息熵 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]