随机系统扇形极点和方差约束状态反馈控制

区域极点和稳态方差是表征控制系统性能的两个重要指标,通过把扇形区域极点指标和稳态方差指标融入到一个修正的Lyapunov方程,研究了扇形区域极点和稳态方差上界约束下的状态反馈控制问题.利用矩阵广义逆和矩阵分解的方法得到了扇形区域极点和稳态方差可配置条件以及控制器的求取方法,所得控制器表达式中含有自由参数,便于控制器的工程实现,也为选取控制器的自由参数来使控制系统满足更多的性能指标留下了余地.     

动态武器目标分配问题的马尔可夫性

动态武器目标分配(weapon target assignment,WTA)问题是军事运筹学研究的重要理论问题,也是作战指挥决策中迫切需要解决的现实问题。在对动态WTA问题进行描述分析的基础上,运用随机过程理论证明了动态WTA过程的马尔可夫性;给出了该马尔可夫决策过程的状态转移概率的解析表达式,并对其状态特点进行了简要分析。研究结果可以为动态WTA及相关问题的研究提供理论和方法依据。     

Robust adaptive UKF based on SVR for inertial based integrated navigation

Aiming at the problem that the traditional Unscented Kalman Filtering (UKF) algorithm can’t solve the problem that the measurement covariance matrix is unknown and the measured value contains outliers, this paper proposes a robust adaptive UKF algorithm based on Support Vector Regression (SVR). The algorithm combines the advantages of support vector regression with small samples, nonlinear learning ability and online estimation capability of adaptive algorithm based on innovation. Firstly, the SVR model is trained by using the innovation in the sliding window, and the new innovation is monitored. If the deviation between the estimated innovation and the measured innovation exceeds a given threshold, then measured innovation will be replaced by the predicted innovation, and then the processed innovation is used to calculate the measurement noise covariance matrix using the adaptive estimation algorithm. Simulation experiments and measured data experiments show that SVRUKF is significantly better than the traditional UKF, robust UKF and adaptive UKF algorithms for the case where the covariance matrix is unknown and the measured values have outliers.     

一种基于博弈论的无线网状网络路由与信道分配联合优化算法

无线网络中的路由与信道分配可极大地影响网络的性能.为了解决无线网状网络中的路由与信道分配问题,提出并研究了一种称为CRAG(基于博弈论的无线网状网络路由与信道分配联合优化)的方法.CRAG采用协同博弈的方式将网络中的每个节点模型化为一个弈者,每个弈者的策略为与其相关的路由与信道分配方案,收益函数为给定流量需求矩阵下的成功传输流量.弈者通过协同博弈来优化收益函数以最大化网络的吞吐量.基于NS3的仿真结果表明,CRAG在收敛性、时延、丢包率和吞吐量方面优于其他当前的算法,从而证明了协同博弈的方法可以用于无线网状网络的路由与信道分配联合优化,并有效地改进网络性能.     

CON/SLK due date assignment and scheduling on a single machine with two agents

We consider scheduling problems involving two agents (agents A and B), each having a set of jobs that compete for the use of a common machine to process their respective jobs. The due dates of the A‐jobs are decision variables, which are determined by using the common (CON) or slack (SLK) due date assignment methods. Each agent wants to minimize a certain performance criterion depending on the completion times of its jobs only. Under each due date assignment method, the criterion of agent A is always the same, namely an integrated criterion consisting of the due date assignment cost and the weighted number of tardy jobs. Several different criteria are considered for agent B, including the maxima of regular functions (associated with each job), the total (weighted) completion time, and the weighted number of tardy jobs. The overall objective is to minimize the performance criterion of agent A, while keeping the objective value of agent B no greater than a given limit. We analyze the computational complexity, and devise polynomial or pseudo‐polynomial dynamic programming algorithms for the considered problems. We also convert, if viable, any of the devised pseudopolynomial dynamic programming algorithms into a fully polynomial‐time approximation scheme. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 416–429, 2016     

联合作战目标协同模型构建与求解方法?

在对联合作战目标协同研究内容分析的基础上,提出了多任务部队与多作战目标的协同目标分配模型,其融合了目标综合价值、目标打击有效性、目标威胁适配度、目标类型匹配、目标距离匹配等因素。通过对各因素量化并将协同目标分配模型转换为指派问题进行求解,验证了分析方法的可行性。     

基于云遗传算法的防空导弹目标分配问题

针对传统遗传算法在防空导弹目标优化分配中存在的缺陷,提出了一种更为有效的云遗传算法。通过该算法对防空导弹目标分配模型进行仿真求解,得出防空导弹射击目标的最优目标分配方案。通过算例分析,云遗传算法相比传统遗传算法收敛速度快,搜索效率高出一倍左右,对产生局部最优解情况进行了合理有效的控制,可操作性、优越性更强。     

一种新的基本概率函数构建方法及应用

针对Dempster-Shafer(D-S)证据推理中基本概率赋值函数的构造问题,基于模糊聚类分析给出了一种新的构建方法.将它应用到雷达目标识别的仿真实验中,并与灰关联法相比较,结果表明该方法切实可行,不仅提高了基本概率赋值的准确性和稳定性,而且利用了数据的结构信息,有效缓解了证据的冲突.     

基于模糊匈牙利算法的炮兵火力单位分配问题

发挥诸火力单位的整体协调优势,寻求在给定约束条件下总的射击效果最好的分配方案,是火力单位最优分配的基本任务.匈牙利算法是求解传统的指派问题的一种较好的方法,运用模糊匈牙利算法在决策过程中将主观因素与客观因素有机地结合起来,解决火力单位分配方案决策中多指标指派问题,从而可以有效地解决炮兵火力单位分配最优化问题.     

基于马尔柯夫过程的武器系统目标分配问题决策分析

将防空作战中武器系统目标分配决策作为一个在动态随机系统中实现最优化的问题,并用马尔柯夫过程理论进行建模分析,提出新的算法并运用Matlab编程;通过对实行不同策略时武器系统长期平均效能的分析比较,指出在目标分配问题上仅靠原有的静态线性规划决策方法是不够的,还必须考虑动态随机对抗过程本身的特性.