排序方式: 共有164条查询结果,搜索用时 15 毫秒
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基于改进TOPSIS法和蚁群算法的反TBM目标群目标分配研究 总被引:1,自引:0,他引:1
基于改进的TOPSIS法和蚁群算法,以弹道导弹目标群为研究对象,研究了反导指控系统对目标群的目标分配问题。首先通过改进的TOPSIS法确定TBM目标群威胁排序并基于拦截排序准则确定拦截排序;其次使作战效能最大化,基于蚁群算法确定目标的最优分配方案;最后通过仿真实例验证了在考虑目标威胁值排序前提下采用此算法,可使目标群分配方案更加科学有效和符合反导作战实际。 相似文献
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在阐述Dempster-Shafer(D-S)证据理论的基础上,较系统地论述了基于D-S证据理论的多传感器雷达体制识别的数据融合方法,并给出了具体的识别实例。实验结果证明了基于多传感器融合后的识别结果明显优于单传感器的识别结果,说明了D-S证据理论的有效性和先进性。 相似文献
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战损装备应急抢修任务指派算法改进研究 总被引:2,自引:0,他引:2
从现代战场及装备保障特点出发,采用十标度法对影响战损装备应急抢修的诸因素加权、归一和排序,给出了一种比较实用、并针对指挥员指挥装备抢修的任务指派算法,使得部署内的所有应急机动装备保障分队避免无故空闲,提高保障力量调配的适时性和任务执行的有效性。 相似文献
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We consider a generalization of the well‐known generalized assignment problem (GAP) over discrete time periods encompassed within a finite planning horizon. The resulting model, MultiGAP, addresses the assignment of tasks to agents within each time period, with the attendant single‐period assignment costs and agent‐capacity constraint requirements, in conjunction with transition costs arising between any two consecutive periods in which a task is reassigned to a different agent. As is the case for its single‐period antecedent, MultiGAP offers a robust tool for modeling a wide range of capacity planning problems occurring within supply chain management. We provide two formulations for MultiGAP and establish that the second (alternative) formulation provides a tighter bound. We define a Lagrangian relaxation‐based heuristic as well as a branch‐and‐bound algorithm for MultiGAP. Computational experience with the heuristic and branch‐and‐bound algorithm on over 2500 test problems is reported. The Lagrangian heuristic consistently generates high‐quality and in many cases near‐optimal solutions. The branch‐and‐bound algorithm is also seen to constitute an effective means for solving to optimality MultiGAP problems of reasonable size. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012 相似文献
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基于特征结构配置的参数化结果,给出了实现结构振动系统模态解耦的充要条件,导出了期望的反馈增益阵及闭环特征向量矩阵的全体参数化表示。该方法在实现模态解耦的同时,还提供了全部自由度,可用来实现系统设计中某些性能要求。该方法直接基于原系统的参数矩阵,便于工程应用。三级质量弹簧系统的实例表明该方法有效。 相似文献