基于模糊神经网络的武器控制系统信号分析

某新型飞机武器控制系统故障诊断过程复杂,故障征兆和故障原因之间存在着许多不确定因素,精确定位故障存在许多困难.针对这种情况,提出了基于模糊神经网络,研究了模糊神经网络技术在武器控制系统故障诊断领域的应用,并根据系统本身的特点,提出了诊断和算法模型.在此基础上,研制出武器控制系统检查仪,对该方法作了验证.结果表明:该方法是可行和有效的.     

An infrared target intrusion detection method based on feature fusion and enhancement

Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning. In view of the characteristics of intrusion targets as well as inspection difficulties, an infrared target intrusion detection algorithm based on feature fusion and enhancement was proposed. This algorithm combines static target mode analysis and dynamic multi-frame correlation detection to extract infrared target features at different levels. Among them, LBP texture analysis can be used to effectively identify the posterior feature patterns which have been contained in the target library, while motion frame difference method can detect the moving regions of the image, improve the integrity of target regions such as camouflage, sheltering and deformation. In order to integrate the advantages of the two methods, the enhanced convolutional neural network was designed and the feature images obtained by the two methods were fused and enhanced. The enhancement module of the network strengthened and screened the targets, and realized the background suppression of infrared images. Based on the experiments, the effect of the proposed method and the comparison method on the background suppression and detection performance was evaluated, and the results showed that the SCRG and BSF values of the method in this paper had a better performance in multiple data sets, and it’s detection performance was far better than the comparison algorithm. The experiment results indicated that, compared with traditional infrared target detection methods, the proposed method could detect the infrared invasion target more accurately, and suppress the background noise more effectively.     

Analysis on MAV/UAV cooperative combat based on complex network

A formation model of manned/unmanned aerial vehicle (MAV/UAV) collaborative combat can qualitatively and quantitatively analyze the synergistic effects. However, there is currently no effective and appropriate model construction method or theory, and research in the field of collaborative capability evaluation is basically nonexistent. According to the actual conditions of cooperative operations, a new MAV/UAV collaborative combat network model construction method based on a complex network is presented. By analyzing the characteristic parameters of the abstract network, the index system and complex network are combined. Then, a method for evaluating the synergistic effect of the cooperative combat network is developed. This method provides assistance for the verification and evaluation of MAV/UAV collaborative combat.     

网络信息战——社交媒体武器化趋势及影响

互联网进入2.0时代后,以社交媒体为主要信息传播平台的新媒体已逐步变为相对独立的社会力量,成为新的战略制高点.社交媒体平台以其传播信息的速度与广度,对庞大的用户人群产生巨大的影响.各国政府和军队意识到社交媒体的飞速发展不仅让其成为新型战略传播工具,更在网络信息战领域成为强大的"心理"干预武器.本文介绍了社交网络通过舆论...     

基于前向型神经网络的空袭目标类型识别模型

对防空作战中空袭目标类型识别问题进行了研究 ,给出了进行目标类型识别的指标集 ,并建立了基于前向型神经网络识别模型 ,实例证明该方法是可行的。     

论多媒体网络教室在教学中的应用

随着信息技术日新月异,多媒体网络教室在教学中的应用越来越广.文章研究多媒体网络教室的类型、设备、功能以及多媒体网络教室在教学中的作用,并提出在应用多媒体网络教室这一教学手段时应注意的几个问题.对合理有效地使用多媒体网络教育具有一定的指导意义.     

NDIS技术在网络监测中的实现

阐述了利用NDIS进行网络包的监测和分析的基本原理,在此基础上实现了对网络适配器在数据链路层网络包的监测,并通过数据报文结构的分析,再现了应用层协议的实现过程.     

一种无师训练的模糊极小极大人工神经网络

提出了一种无师训练的fuzzymin max人工神经网络,它兼有一般fuzzymin max网与ART2网的优点,既弥补了fuzzymin max网不能自适应在线学习新类的缺陷,又消除了ART2网警戒门限过高的弊病.经模式识别仿真对比,对同样的输入数据,文中提出的网络用较低的警戒门限值即可达到ART2用很高的警戒门限值才能达到的分类效果,且计算量大大减少.得到的结论是:对模式识别而言,文中提出的网络比fuzzymin max网和ART2网更具有实用价值.     

核动力装置控制系统神经网络故障诊断

研究了如何将先进的人工神经网络的方法应用到核动力控制系统故障诊断中,并对这种方法的原理、优缺点、可行性等进行了分析,提出了针对核动力装置控制系统的设计方案.