高分辨率雷达目标一维距离像的编码识别算法

文中提出了一种高分辨率雷达目标一维距离像的识别算法。该算法借用图像处理方法,先对雷达目标的一维距离像进行编码,经过傅里叶变换提取一组形状特征。这组特征精确地描述了一维距离像曲线的走向。而后,利用人工神经网络技术对一定姿态角变化范围内的这组形状特征进行识别。实验结果表明,可以获得良好的识别效果。该算法为雷达目标一维距离像识别算法的实时处理提供了一条有效途径。     

基于人工神经网络的操作手IKP 求解

人工神经网络以其信息的并行处理与分布式存储、自组织和自适应等特点,在机器人领域显示了极大的应用潜力。本文探讨人工神经网络在机器人操作手ＩＫＰ求解中的应用,试图求得操作手ＩＫＰ的一组或所有可行解。文章利用ＢＰ网络对操作手ＩＫＰ的求解进行了讨论和分析,并给出了具体的求解实例。     

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。     

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 .     

RBF神经网络在单轴旋转惯导系统轴向陀螺漂移辨识中的应用

在激光陀螺单轴旋转惯性导航系统中,单轴旋转可以自动补偿垂直于旋转轴上的惯性器件误差,却不能消除旋转轴方向上惯性器件的误差,因此单轴旋转惯性导航系统的导航精度主要由轴向陀螺漂移决定.提出了一种基于径向基函数神经网络的轴向陀螺漂移辨识方法,利用系统纬度误差和温度变化量作为训练集,针对系统热态、冷态两种情况对RBF神经网络进行训练,对轴向陀螺漂移的辨识精度达到0.0003°/h.试验结果表明:该方法能够有效地辨识轴向陀螺漂移,使系统达到较高的导航精度,满足实际应用的需要.     

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。     

I-TCP:一个高速长距离网络的AIMD算法

针对互联网中高速长距离网络上的拥塞控制问题,基于同步AIMD源在Drop-Tail网络环境下竞争带宽的网络模型,提出了一个新的AIMD拥塞控制算法,I-TCP。通过合理地设计高速和低速模式以及模式切换规则,使得该算法适合于配置到高速长距离网络,同时在传统低速网络中也具有很好的性能。仿真试验表明,I-TCP中的AIMD算法在高速长距离网络中表现出了很好的效率、公平性和网络响应性。     

MANET多路径路由中最大可靠性路径选择算法

如何选择路径的数量和质量对多路径路由机制的性能有着重要的影响。已有的多路径算法没有深入研究如何选择多路径的问题。对目前存在的两个典型问题进行了分析,在此基础上研究了路径可靠性模型和虚拟完全非交叉多路径模型,然后提出一个最大可靠性多路径选择算法。算法利用路径权重作为路径可靠性的近似解决方案,以此克服路径可靠性度量问题(NP难题)研究的复杂性,根据路径可靠性模型和完全非交叉多路径模型来选择可靠的路径集,使用这组路径集并行分布流量。应用OPNET模拟平台实现了算法,结果表明,本算法能增加聚合带宽,优化网络带宽的应用,提高网络的吞吐率和多路径路由的性能。