ID 原文 译文
26555 针对人工鱼群算法(artificial fish swarm algorithm，AFSA)多峰寻优能力不足的问题，提出了一种免疫人工鱼群网络算法。 In view of the artificial fish algorithm (artificial fish swarm algorithm, AFSA) the problem of insufficient multimodal optimization ability, a kind of artificial fish immune network algorithm was presented.
26556 应用改进的觅食行为，提升了算法的局部寻优能力；采用免疫网络调节机理，保持了人工鱼群多样性，不断探寻新的局部峰值；执行模式搜索法(pattern search method，PSM)，完成精英人工鱼群的精细搜索。 Application of improved foraging behavior, improve the local optimization ability of the algorithm;By using the immune network regulating mechanism, to keep the diversity of artificial fish constantly explore new local peak;Execution pattern search method (the pattern search method, PSM), complete the elite fine search of artificial fish.
26557 仿真实验结果表明，该算法具有较强全局优化能力和局部优化能力，且搜索到每个最优解都达到了理想值。 The simulation results show that the algorithm has strong global optimization ability and local optimization ability, and to search for the best solution to each all reach the ideal value.
26558 针对资源有限的传感器网络中目标动态跟踪问题，提出了一种能够自适应选择跟踪传感器的机动目标协同跟踪算法。 In view of the limited resources of dynamic target tracking problem in sensor networks, this paper proposes a to select adaptive tracking sensor of maneuvering target tracking algorithm together.
26559 首先，采用粒子群优化算法优化传感器网络能耗与有效覆盖率，进行传感器位置部署； First of all, the energy consumption of sensor network was optimized by using particle swarm optimization algorithm and effective coverage, with position sensor deployment;
26560 然后，以最大化候选传感器的Rényi信息增量与最小化传感器间信息传递能耗为适应度函数，采用二进制粒子群优化算法自适应选择最佳跟踪传感器组； Then, to maximize the candidate sensor Renyi information increment and minimize energy consumption of sensor information transfer between as fitness function, adopting adaptive binary particle swarm optimization algorithm to choose the best tracking sensor group;
26561 最后，利用交互多模型粒子滤波对机动目标位置进行估计并进行分布式融合。 Finally, using the interacting multiple model particle filter for maneuvering target position and estimate for the distributed fusion.
26562 仿真结果表明，与现有方法相比，该方法可在非高斯非线性环境下自适应选择最优跟踪传感器，显著提高目标跟踪精度，降低网络能耗。 Simulation results show that compared with the existing methods, this method in nonlinear non-gaussian environment adaptive choose optimal tracking sensor, significantly improve the target tracking accuracy, reduce the network energy consumption.
26563 针对一类受干扰影响的不确定线性系统，研究了基于高阶比例积分(proportional integral，PI)观测器的鲁棒故障检测设计问题。 For a class of uncertain linear systems affected by the interference, is studied based on high order PI (proportional integral, PI) observer design problem of robust fault detection.
26564 通过对一类Sylvester矩阵方程的参数化解，将基于高阶PI观测器的鲁棒故障检测问题转化为具有多约束的优化问题。 Parameters of Sylvester matrix equation to solve, the high-order PI observer based robust fault detection problem is transformed into optimization problem with multiple constraints.