基于在线聚类的机器人自适应模糊建模

针对机器人这种具有时变、强耦合和不确定性的复杂非线性被控对象,提出一种基于在线聚类的模糊自适应方法用于机器人系统建模。建模过程中采用在线聚类算法辨识机器人T-S模型的前提参数,采用递推最小二乘算法(RLSE)辨识结论参数,根据过程中新的数据信息,模糊规则可以自动增加、修改和删除,实现了模型结构和参数的在线辨识和更新,而且该方法具有模型结构简单、建模速度快、精度高等优点。最后通过二自由度机器人仿真研究证明了该方法的有效性和优越性。     

自然计算——人工智能的有效实施模式

对自然计算理念给出统一的框架描述,并以反馈式神经网络和群体智能算法为例加以具体论述.在所定义模型框架下,自然计算领域内算法中的相关智能体群表现出一种相对统一的智能计算模式.     

武器装备体系对抗的一种研究方法

武器装备对抗的体系化是信息化战场的显著特征,然而体系对抗过程的复杂性使得体系作战效能研究相对较为困难。基于系统工程思想提出了一种体系对抗的研究方法,方法将研究内容分为四个层次:对抗交互层、系统损伤层、功能层、效能层,首先给出了各层次的定义。然后对该研究方法的思想和步骤进行了详细的阐述,通过建立各层次描述指标,研究层次间相互关系,在此基础上从分析体系对抗的物理规律入手,采用定量与定性相结合,依次完成各层指标的推理和计算,得到对抗条件下武器装备体系的作战效能。最后以导弹防御体系与具有突防能力的某导弹武器系统的对抗为例,验证了方法的有效性。     

Adaptive immune-genetic algorithm for global optimization to multivariable function

An adaptive immune-genetic algorithm(AIGA)is proposed to avoid premature convergence and guarantee the diversity of the population.Rapid immune response (secondary response),adaptive mutation and density operators in the AIGA are emphatically designed to improve the searching ability,greatly increase the converging speed,and decrease locating the local maxima due to the premature convergence.The simulation results obtained from the global optimization to four multivariable and multi-extreme functions show that AIGA converges rapidly,guarantees the diversity,stability and good searching ability.     

嫦娥二号平动点间转移轨道方案研究

针对我国嫦娥二号卫星拓展任务需求,研究了卫星由日地L2点向L1点的转移轨道方案。基于卫星真实星历,考虑各种摄动因素,借助不变流形理论和微分修正方法,设计了嫦娥二号在日地平动点L1和L2之间的转移轨道,提出了便于理解和工程应用的平动点间直接转移方式,研究了真实力模型下平动点Lissajous轨道的不变流形转移方式,对比了两种转移方式的出发速度增量、转移时间与出发时刻的关系。研究表明,嫦娥二号可以通过直接转移方式或者借助不变流形进入日地L1点Lissajous轨道,直接转移方式下有42天出发机会,基于不变流形方式下最短转移时间为210天,对应速度增量5.776 m/s,最小速度增量可达0.024 m/s。研究结果可直接应用于我国嫦娥二号拓展任务的轨道设计与控制,并为我国后续平动点等深空探测任务提供有益借鉴.     

基于NDO的飞机轨迹跟踪抗干扰控制律设计

针对飞机纵向欠驱动耦合动力学模型设计鲁棒轨迹跟踪滑模控制律。首先,通过引入虚拟状态变量进行状态变换实现模型控制解耦。其次,考虑由系统建模误差和大气干扰组成的复合不确定性,设计非线性干扰观测器(nonlinear disturbance observer, NDO)进行观测补偿。然后,建立飞机极短距起降中攻角变化轨迹曲线,设计滑模控制律实现对虚拟状态变量和攻角轨迹指令的稳定跟踪。仿真结果表明,NDO可以实现对系统不确定性的精确观测和补偿,滑模控制律具有对系统建模误差和大气风切变的鲁棒性和抗干扰性。     

基于压缩感知测量值的扩展目标检测

针对宽带雷达扩展目标检测这一问题,提出了一种在复高斯白噪声背景下基于压缩感知(compressed sensing, CS)测量值的检测新方法。新方法将CS理论引入到宽带雷达扩展目标检测领域,首先通过构造sinc基来稀疏表示扩展目标的一维距离像(high resolution range profile, HRRP),再由复杂的近似消息传递(complex approximate message passing, CAMP) 算法从被复高斯白噪声污染的CS测量值中得到HRRP由sinc基线性表示的相关系数,最后由基于l-0范数的检测器实现扩展目标检测,同时经过推导得到虚警概率和检测概率。基于实测宽带雷达回波数据的实验结果表明,所构造的sinc基可以较好地稀疏表示扩展目标的HRRP;和传统的检测器相比,所提出的新方法可以更好地实现扩展目标检测。     

基于交互式多模型算法跟踪临近空间目标

由于目前机动目标模型越来越向模块化、并行计算的方向发展,对目前算法计算效率提出了更高的要求。对于临近空间超声速机动目标一般采用多种机动模型跟踪,单一模型已经很难满足高精度跟踪的需要。因此需要使用基于多种模型进行交叉耦合的交互式多模型（interacting multiple model, IMM）算法,这种算法特点与临近空间目标高速、高机动特性相适应。同时考虑到扩展卡尔曼滤波 (extended Kalman filter, EKF) 算法对强非线性对象滤波效果不好, 无迹卡尔曼滤波(unscented Kalman filter, UKF)算法对于此类问题,可以很好地加以解决。仿真对比试验表明,交互式多模型〖CD*2〗无迹卡尔曼滤波(interacting multiple model unscented Kalman filter, IMM UKF)算法优于单一模型EKF算法。     

Opportunistic spectrum sharing in software defined wireless network

Over the past few decades, the world has witnessed a rapid growth in mobile and wireless networks（MWNs） which significantly change human life. However, proliferating mobile demands lead to several intractable challenges that MWN has to face. Software-defined network is expected as a promising way for future network and has captured growing attention. Network virtualization is an essential feature in software-defined wireless network（SDWN）, and it brings two new entities, physical networks and virtual networks. Accordingly, efficiently assigning spectrum resource to virtual networks is one of the fundamental problems in SDWN. Directly orienting towards the spectrum resource allocation problem, firstly, the fluctuation features of virtual network requirements in SDWN are researched, and the opportunistic spectrum sharing method is introduced to SDWN. Then, the problem is proved as NP-hardness. After that, a dynamic programming and graph theory based spectrum sharing algorithm is proposed.Simulations demonstrate that the opportunistic spectrum sharing method conspicuously improves the system performance up to around 20%–30% in SDWN, and the proposed algorithm achieves more efficient performance.     

Polynomial-rooting based fourth-order MUSIC for direction-of-arrival estimation of noncircular signals

A polynomial-rooting based fourth-order cumulant algorithm is presented for direction-of-arrival（DOA） estimation of second-order fully noncircular source signals, using a uniform linear array（ULA）. This algorithm inherits all merits of its spectralsearching counterpart except for the applicability to arbitrary array geometry, while reducing considerably the computation cost.Simulation results show that the proposed algorithm outperforms the previously developed closed-form second-order noncircular ESPRIT method, in terms of processing capacity and DOA estimation accuracy, especially in the presence of spatially colored noise.