再入机动飞行器自适应轨迹线性化控制

针对一类多输入多输出模型不确定系统,提出了一种基于广义模糊神经网络的自适应轨迹线性化控制方法(ATLC).针对再入机动飞行器(MRV)进行了控制器设计和分析.MRV气动参数存在较大的不确定,这会导致轨迹线性化控制器(TLC)鲁棒性能下降.利用广义模糊神经网络(G-FNN)在线补偿系统的非线性建模不确定,改善了控制器性能.基于Lyapunov稳定性理论,证明了ATLC闭环控制系统的稳定性.仿真结果表明自适应轨迹线性化控制系统在飞行器气动参数大范围摄动时仍具有鲁棒性和稳定性,验证了所提出的控制策略的有效性.     

Relay selection in cooperative communication systems over continuous time-varying fading channel

In this paper, we study relay selection under outdated channel state information (CSI) in a decode-and-forward (DF) cooperative system. Unlike previous researches on cooperative commu-nication under outdated CSI, we consider that the channel varies continuously over time, i.e., the channel not only changes between relay selection and data transmission but also changes during data transmission. Thus the level of accuracy of the CSI used in relay selection degrades with data transmission. We first evaluate the packet error rate (PER) of the cooperative system under contin-uous time-varying fading channel, and find that the PER performance deteriorates more seriously under continuous time-varying fading channel than when the channel is assumed to be constant during data transmission. Then, we propose a repeated relay selection (RRS) strategy to improve the PER performance, in which the forwarded data is divided into multiple segments and relay is reselected before the transmission of each segment based on the updated CSI. Finally, we propose a combined relay selection (CRS) strategy which takes advantage of three different relay selection strategies to further mitigate the impact of outdated CSI.     

基于动态逆和神经网络的机动弹头姿态控制系统设计

机动弹头是复杂的非线性系统,它给姿态控制系统设计人员以极大的挑战。为了满足飞行稳定性要求,提高系统的适应性和鲁棒性,本文构造了动态逆神经网络姿态控制系统,设计了慢变量和快变量动态逆,引入了在线神经网络进行逆误差的补偿。实例仿真的结果表明:动态逆和在线神经网络的结合,能够实现弹头的稳定飞行。     

BP神经网络理论在模型修正的应用

神经网络由于其本身具有的优越性,已广泛应用于各个领域中的分类、联想问题。将神经网络理论用于对利用有限元分析方法得到的系统模型进行修正,从计算结果看,该方法收敛速度快且收敛到全局最优解。     

Improved predictions of equatorial TEC for real time applications

It is important to use models developed specifically for the equatorial ionospheric estimation for real-time applications, particularly in Satellite Navigation. This work demonstrates a methodology for improved predictions of VTEC in real time using the model developed for the equatorial ionosphere by the authors. This work has been done using TEC data of the low solar activity period of 2005 obtained using dual frequency GPS receivers installed under the GAGAN project of ISRO. For the purpose, the model is first used in conjunction with Kriging technique. Improvement in accuracy is observed when compared with the estimations from the model alone using the measurements as true reference. Further improvement is obtained by Bayesian combination of these estimates with independent Neural Network based predictions. Statistical performance of improvement is provided. An improvement of ∼1 m in confidence level of estimation of VTEC is obtained.     

企业网络：类型比较及发展研究

分析了作为中间组织类型的企业网络所具有的组织结构和组织特征，并对其进行了三个层次的划分：集群层次、集团层次和联盟层次，比较分析了处于各个不同层次上的企业网络之间的差异性。认为，技术平台和合作平台将成为企业网络今后发展和进化的关键，并探讨了其相应的两个方面：虚拟创新和信任。     

分布式交互仿真技术的理论与实现

分布式交互仿真（简称ＤＩＳ）是现代仿真科技的前沿课题之一，已广泛受到到以美国为代表的西方发达国家的高度重视，并开展了深入研究。我国在该领域的研究才刚刚起步。ＤＩＳ是指运用计算机网络协议将分布于不同地域仿真模拟器连接起来，营造一个大范围的虚拟环境（ＬＳＶＥ）。在这个环境下，分布于各地的用户可进行诸如空中格斗、空地对抗、地面对抗等各种军事模拟训练。本文介绍了分布式交互仿真的发展历程与发展现状，分析了仿     

Vision-based bio-inspired guidance law for small aerial vehicle

During predation, a flying insect can form a stealth flight path. This behavior is called motion camouflage. Based on the study results of this behavior, the perception and neurology of flying insects, a novel bio-inspired guidance law is proposed for the terminal guidance for small aerial vehicle with charge-coupled device imaging seekers. The kinematics relationship between a small aerial vehicle and target is analyzed, and a two-dimensional guidance law model is established by using artificial neural networks. To compare with the proportional guidance law, the numerical simulations are carried out in the vertical plane and in the horizontal plane respectively. The simulation results show that the ballistic of the small aerial vehicle is straighter and the normal acceleration is smaller by using the bio-inspired guidance law than by using the proportional guidance law. That is to say, the bio-inspired guidance law just uses the information of the target from the imaging seeker,but the performance of it can be better than that of the proportional guidance law.     

Optimization of beamforming and path planning for UAV-assisted wireless relay networks

Recently, unmanned aerial vehicles （UAVs） acting as relay platforms have attracted considerable attention due to the advantages of extending coverage and improving connectivity for long-range communications. Specifically, in the scenario where the access point （AP） is mobile, a UAV needs to find an efficient path to guarantee the connectivity of the relay link. Motivated by this fact, this paper proposes an optimal design for beamforming （BF） and UAV path planning. First of all, we study a dual-hop amplify-and-forward （AF） wireless relay network, in which a UAV is used as relay between a mobile AP and a fixed base station （BS）. In the network, both of the AP and the BS are equipped with multiple antennas, whereas the UAV has a single antenna. Then, we obtain the output signal~to-noise ratio （SNR） of the dual-hop relay network. Based on the criterion of maximizing the output SNR, we develop an optimal design to obtain the solution of the optimal BF weight vector and the UAV heading angle. Next, we derive the closed-form outage probability （OP） expression to investigate the performance of the dual-hop relay network conveniently. Finally, computer simulations show that the proposed approach can obtain nearly optimal flying path and OP performance, indicating the effectiveness of the proposed algorithm. Furthermore, we find that increasing the antenna number at the BS or the maximal heading angle can significantly improve the performance of the considered relay network.     

基于模块度的复杂网络社团结构聚类新算法

针对传统的分裂聚类算法删除连边会影响节点划分的问题,结合K-means算法的思想并加以改进,提出了一种以邻居节点为聚类备选集,所有社团中心节点同时聚类的同步聚类算法。该算法根据节点中心度和最短路变化率确定社团中心节点集,然后以中心节点为社团中心,以邻居节点为聚类备选集合进行聚类,完成社团的划分。将算法应用到Zachary网络中并与GN算法、Top Leader算法进行比较,仿真结果表明该算法对网络有较好的划分。