ID 原文 译文
55397 该算法保留了狼群算法基于职责分工的协作式搜索特性,选取离散空间的经典问题——0-1背包问题进行仿真实验,具体通过10组经典的背包问题算例和BWPA算法与经典的二进制粒子群算法、贪婪遗传算法、量子遗传算法在求解3组高维背包问题时的对比计算,例证了算法具有相对更好的稳定性和全局寻优能力。 This algorithm retains the wolves algorithm based on the division of duties of collaborative search feature, select the classic problem of discrete space - simulation experiment 0-1 knapsack problems, the concrete by 10 classic knapsack problem set an example and BWPA algorithm and classical binary particle swarm optimization (pso) algorithm, the greedy genetic algorithm and the quantum genetic algorithm in solving three groups contrast calculation when high-dimensional knapsack problem, exemplify algorithm has relatively better stability and global optimization ability.
55398 提出机群多编队协同作战复杂网络模型,基于复杂网络自同步原理,研究网络节点动力学和耦合强度给定不变情况下,网络拓扑结构对作战网络同步能力的影响。 Fleet formation to operate with more complex network model is put forward, based on complex network self synchronous principle, research network node dynamics and the coupling strength given the same circumstances, the network topology to combat the influence of the network synchronous ability.
55399 针对作战网络特点,以最大化网络同步能力和不同层级指控节点效能为优化目标,作战网络指控结构和作战环境对通信拓扑的限制为约束条件,建立作战网络自同步优化模型。 According to the characteristics of operational networks at different levels to maximize the network synchronous ability and accused of nodes as the optimization goal of efficiency, operational network charge structure and the operating environment of communication topology restrictions as constraint conditions, operational network self synchronous optimization model is established.
55400 采用遗传算法,通过调整作战节点之间的通信结构对上述模型求解。 Using genetic algorithm, by adjusting the operational node communication between the structure of the above model.
55401 仿真实验表明,获得的优化网络在保持作战网络结构特点的同时,具备更小的网络特征值比和更快的一致性收敛速度。 Simulation experiments show that the optimized network while maintaining the operational network structure characteristics, with the network characteristics of smaller value than the consistency and faster convergence speed.
55402 针对目前多输入多输出(multiple-input multiple-output,MIMO)雷达中通常基于某一种而不是整体性能的波形优化问题,提出一种提高MIMO雷达检测和参数估计性能的波形优化方法。 Aiming at the multiple input multiple output (multiple input multiple output, MIMO) radar is usually based on a certain rather than the overall performance of waveform optimization problem, put forward a kind of to improve the performance of MIMO radar detection and parameter estimation waveform optimization method.
55403 该方法基于提高检测概率、降低参数估计方差及抑制旁瓣3种性能约束优化发射波形相关矩阵(waveform covariance matrix,WCM)。 The method based on parameter estimation variance and improve the detection probability and reduce three performance constraint optimization restrain sidelobe launch waveform correlation matrix (waveform covariance matrix, WCM).
55404 首先推导了检测概率及克拉美-罗界(Cramer-Rao bound,CRB)的等价表达式,而后联合最大化主旁瓣差约束,并对各约束条件加权,从而得到可灵活调整加权系数以满足实际应用中不同需要的波形优化问题。 First, and the detection probability is deduced from the Latin America - ROM community (Cramer - Rao bound, the CRB) of equivalent expressions, and then combined to maximize the sidelobe constraint, and the constraint conditions of weighted, flexible adjustment of the weighted coefficient is obtained in order to meet the different needs of waveform optimization problems in actual application.
55405 此波形优化问题可表示为线性规划问题,因而可高效求解。 The shape optimization problem can be expressed as a linear programming problem, and thus can be efficient to solve.
55406 仿真实验对3种约束条件对雷达性能的影响进行了详细分析,实验结果验证了所提方法的有效性。 Simulation experiments of three kinds of constraint conditions affect the performance of radar are analyzed in detail, the experimental results verify the effectiveness of the proposed method.