摘要
提出了一种基于改进小波神经网络优化参数的指挥信息系统安全态势预测方法.该方法通过改进的小生境遗传算法构建了基于小波神经网络优化参数的网络安全状况预测模型,利用自适应遗传算法对小波神经网络参数进行调优,增强其搜索能力.同时,为解决自适应遗传算法容易陷入局部最优且收敛速度慢的问题,提出利用小生境技术和模糊聚类技术来处理.仿真实验结果表明,与传统算法相比,所提算法具有更快的收敛速度和更好的预测精度.
A method for predicting the security situation of command and information systems based on improved parameters of improved wavelet neural networks is proposed.This method constructs a network security situation prediction model based on wavelet neural network optimization parameters through improved niche genetic algorithm,and uses adaptive genetic algorithm to tune the wavelet neural network parameters to enhance their search capabilities.At the same time,in order to solve the problems that the adaptive genetic algorithm easily falls into the local optimum and the convergence speed is slow,the niche technology and the fuzzy clustering technique are proposed to deal with the problems.Simulation results show that compared with the traditional algorithm,the proposed algorithm has faster convergence rate and better prediction accuracy.
作者
宋志远
SONG Zhi-yuan(Institute of Information Engineering,Wuchang Institute of Technology,Wuhan 430065,China)
出处
《内蒙古师范大学学报（自然科学汉文版）》
CAS
2018年第4期295-299,共5页
Journal of Inner Mongolia Normal University(Natural Science Edition)
基金
湖北省教育厅科学研究计划项目(B2016353)
关键词
指挥信息系统
安全态势预测
小波神经网络
小生境遗传算法
command information system
security situation prediction
wavelet neural network
niche genetic algorithm
