基于改进差分进化算法的内弹道参数优化

火炮内弹道参数直接影响其计算结果,内弹道参数优化设计是内弹道分析计算的重要环节。基于经典内弹道计算模型,以火药内弹道计算中最为敏感的燃速指数及其燃速系数为设计参量,以最大膛压及弹丸初速的计算值与测试值一致性为目标,对差分进化算法群体的初始化、变异因子及变异模式进行了改进,对某型火炮的内弹道参数进行了优化计算。优化设计结果表明改进后的差分进化算法满足内弹道设计工程实践要求,为内弹道参数优化设计提供了一种有效的算法。     

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     

防空武器系统仿真中的三维图形实时成像技术

图形图像技术在仿真中的应用除使之高度可视化外,更重要的是引入了“三维描述”的概念,扩大了“定量”     

多源信息的Bayes融合精度鉴定方法

为了合理运用多种验前信息 ,文中首先引入了验前信息的可信度的概念 ,由此论述了验前信息与现场试验信息的融合方法 ,给出了多源信息下的Bayes精度鉴定方案。最后以实例说明鉴定方案的运用。     

钟敬文的文化观与新疆民族文化资源的保护利用

钟敬文阐明了民族的整体文化与民俗文化的关系,确定了民俗文化在整体文化中的重要地位。他指出文化认同是实施西部大开发战略的首要前提。钟敬文的文化观对保护和利用新疆民族文化资源,推动新疆经济文化建设具有指导意义。     

基于小波变换模极大值恢复的信号消噪方法

大多数的信号常具有某种奇异性 ,并携带有随机噪声 .首先回顾了信号奇异性与小波变换模极大值之间的关系 ,然后利用信号与噪声的Lipschitz指数的符号不同 ,在S .Mallat等人工作的基础上 ,给出了从信号中去除噪声的具体算法     

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.     

运载火箭伺服机构故障检测与诊断的扩展多模型自适应方法

针对运载火箭伺服机构故障,提出一种基于扩展多模型自适应估计的故障检测与诊断算法。建立考虑伺服机构故障的运载火箭姿态动力学模型;将故障角度作为状态变量得到增广状态空间模型;利用扩展卡尔曼滤波器进行状态向量和故障参数的非线性估计,并基于传感器测量数据采用假设检验算法在线计算故障发生的概率;给出基于扩展多模型自适应估计的故障检测与诊断算法流程。仿真结果表明,该方法在无故障时可对伺服机构进行健康监测;在单台伺服机构故障下,可以及时准确判断出哪一台芯级伺服机构发生故障,并可准确估计出伺服机构故障下的发动机摆角角度。     

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