防空C3I雷达情报网数据融合算法的研究

传统 C3I系统的数据融合算法运算量大 ,融合精度不高。根据我国防空 C3I系统的现状 ,提出了一种新的基于神经网络的防空 C3I系统数据融合算法。由于神经网络是全并行工作 ,所以此算法具有处理速度快、精度高等优点。仿真结果表明 ,航迹交接算法和航迹融合算法的性能都比较好 ,对真实雷达网数据融合有很好的参考价值     

基于BP神经网络的C4ISR系统人机交互性能评估

针对C4ISR系统中人机交互子系统的特点,运用神经网络方法探索其性能评估问题.建立了C4ISR系统人机交互性能评估的指标体系,阐述了BP神经网络的相关原理及算法,构建了基于此网络的性能评估模型.并利用MATLAB软件进行了仿真分析,证明了理论的正确.采用神经网络理论进行性能评估,可弥补专家的经验和知识,降低评价过程中的人为因素影响,保证其客观性.模型能够比较准确地对C4ISR系统人机交互性能进行评估,为研究指挥自动化系统提供了一种新的思路.     

Analysis of production lines with Coxian service times and no intermediate buffers

This paper uses the holding time model (HTM) method to derive an approximate analytic formula for the calculation of the mean throughput of a K-station production line with no buffers between any two successive stations. Service times follow the two-stage Coxian (C²) distribution at all stations. The paper provides a formula that relates the third moment of the service completion (or virtual service) time with the respective parameters of the service time, the repair time and the time to breakdown (the latter is assumed to follow the exponential distribution). In this way, it concludes that under certain conditions the two-stage Coxian distribution can be used to approximate any general distribution matching the first three moments of the service completion time distribution. The mean holding times (consisting of the service and blocking periods) of all stations of the line are obtained in an analytical form. Numerical results are provided for the mean throughput of lines with up to 20 stations. These results are shown to have a good accuracy compared against results obtained from the Markovian state method (for short lines) and results from simulation (for longer lines). © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 669–685, 1998     

The effect of training data set size and the complexity of the separation function on neural network classification capability: The two-group case

Classification among groups is a crucial problem in managerial decision making. Classification techniques are used in: identifying stressed firms, classifying among consumer types, and rating of firms' bonds, etc. Neural networks are recognized as important and emerging methodologies in the area of classification. In this paper, we study the effect of training sample size and the neural network topology on the classification capability of neural networks. We also compare neural network capabilities with those of commonly used statistical methodologies. Experiments were designed and carried out on two-group classification problems to find answers to these questions. The prediction capability of the neural network models are better than traditional statistical models. The learning capability of the neural networks is improving compared to traditional models because the discriminate function is more complex. For real world classification problems, the usage of neural networks is highly recommended, for two reasons: learning capability and flexibility. Learning capability: Neural network classifies better in sterile experiments as performed in this research. Flexibility: Real life data are rarely not contaminated with noise, such as unknown distributions, and missing variables, etc. Neural networks differ from a statistical model that it is not dependent on any assumption concerning the data set distribution. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 699–717, 1997     

基于RBF网络的导弹攻击区解算及对比分析

研究了神经网络在空空导弹攻击区处理中的运用,在提出多层前馈(MF)网络和径向基函数(RBF)网络设计方案的基础上,着重对MF网络的BP算法、改进BP算法以及RBF网络的应用进行了仿真,并从收敛速度、逼近精度和实时性三个方面,对计算结果进行了对比分析,结论表明RBF网络在导弹攻击区处理中具有更高的可行性,更适用于快速高精度的火控解算。     

BP人工神经网络的应用及其实现技术

简述了BP人工神经网络的算法原理 ,利用Batchnet程序对一个三层BP人工神经网络进行了设计 ,应用Matlab中人工神经网络工具箱设计了一个用于函数逼近的BP人工神经网络 ,最后 ,运用Matlab中可视化工具Simulink对一个BP人工神经网络例子进行了仿真 ,通过例子 ,探讨了BP人工神经网络的应用并介绍目前几种实用的BP人工神经网络实现技术 .     

一种基于神经网络的磁性目标定位方法

磁性目标定位问题可归结为一类非线性规划问题的求解 .该问题的最大特点就是其目标函数的计算过程极为繁琐 .而目标函数计算的快慢对磁定位的实时性有很大影响 .在详细研究了目标函数之后 ,给出了目标函数的一种神经网络结构实现 .由于该网络结构易于并行计算和VLSI实现 ,从而可使磁定位的实时性得到改善 .     

导弹故障智能诊断的一种实现方法

研究了基于神经网络的故障诊断专家系统的理论和方法及具体实现过程。将此方法应用于导弹武器系统故障智能诊断之中 ,结果表明该方法是一种行之有效的故障智能诊断方法。     

跨模态检索中的相似性漂移问题

为了降低“相似性漂移”问题的影响,提出一种基于“邻域传播”的匹配策略,将待查询项的模态内近邻映射到目标空间中,并将它们在目标空间中的最近邻作为查询项的跨模态近邻。基于邻域传播的匹配策略在不改变跨模态映射函数的条件下,可以有效地降低“相似性漂移”带来的误匹配现象。理论和实验分析证明,跨模态映射函数的“相似性漂移”问题广泛存在,而基于“邻域传播”的匹配策略可以有效降低其影响,提高匹配的准确率。     

图像超分辨率卷积神经网络加速算法

为了实现模型的实时和嵌入式运行,提出了一种轻量级的卷积神经网络结构。通过采用较小的滤波器尺寸和引入深度可分离卷积,可大量减少模型参数,提高模型非线性表达能力;在网络末端引入子像素卷积层,直接从原始低分辨率图像学习到高分辨率图像的映射,计算成本为原来的1/k²(k为放大因子)。在Set5数据集上的实验表明,所提模型的速度较经典的图像超分辨率重建算法速度提高了25.8倍,能够在通用GPU上实时运行,峰值信噪比平均提高了0.17 dB,并且参数只有它的35%。