研究了多通道分布式主动噪声控制(Distributed Active Noise Control,ANC)系统的空间平滑问题.传统的分布式ANC算法通过本地控制器之间的通信,可以大大提高系统稳定性.但由于每组控制器和误差麦克风分布位置不同,引入的估计偏差影响系...研究了多通道分布式主动噪声控制(Distributed Active Noise Control,ANC)系统的空间平滑问题.传统的分布式ANC算法通过本地控制器之间的通信,可以大大提高系统稳定性.但由于每组控制器和误差麦克风分布位置不同,引入的估计偏差影响系统整体降噪性能.因此,旨在开发一种新型扩散滤波最小均方算法(Diffusion Filtered-x Least Mean Squares,Diff-FxLMS),该算法平衡了空间平滑度和信息交换强度之间的矛盾,从而减少估计偏差.通过对Diff-FxLMS算法性能进行的理论分析,揭示了扩散控制机制,为ANC算法设计提供了理论依据,并在此基础上发展出一种新型的可变平滑度的Diff-FxLMS(Varible Spatial Regularized Diff-FxLMS,VSR-Diff-FxLMS)算法.仿真结果验证了新算法的性能及理论分析的可靠性.展开更多
This paper extends the single-task n-Vehicle Exploration Problem to Multitask n-Vehicle Exploration Problem (MTNVEP), by combining n-Vehicle Exploration Problem with Job Scheduling Problem. At first, the authors pro...This paper extends the single-task n-Vehicle Exploration Problem to Multitask n-Vehicle Exploration Problem (MTNVEP), by combining n-Vehicle Exploration Problem with Job Scheduling Problem. At first, the authors prove that MTNVEP is NP-hard for fixed number of tasks, and it is strongly NP-hard for general number of tasks. Then they propose an improved accurate algorithm with computing time O(n3n), which is better than O(n!) as n becomes sufficiently large. Moreover, four heuristic algorithms are proposed. Effectiveness of the heuristic algorithms is illustrated by experiments at last.展开更多
文摘研究了多通道分布式主动噪声控制(Distributed Active Noise Control,ANC)系统的空间平滑问题.传统的分布式ANC算法通过本地控制器之间的通信,可以大大提高系统稳定性.但由于每组控制器和误差麦克风分布位置不同,引入的估计偏差影响系统整体降噪性能.因此,旨在开发一种新型扩散滤波最小均方算法(Diffusion Filtered-x Least Mean Squares,Diff-FxLMS),该算法平衡了空间平滑度和信息交换强度之间的矛盾,从而减少估计偏差.通过对Diff-FxLMS算法性能进行的理论分析,揭示了扩散控制机制,为ANC算法设计提供了理论依据,并在此基础上发展出一种新型的可变平滑度的Diff-FxLMS(Varible Spatial Regularized Diff-FxLMS,VSR-Diff-FxLMS)算法.仿真结果验证了新算法的性能及理论分析的可靠性.
基金partly supported by Daqing Oilfield Company Project of PetroCHINA under Grant No.dqc- 2010-xdgl-ky-002Key Laboratory of Management,Decision and Information Systems,Chinese Academy of Sciences
文摘This paper extends the single-task n-Vehicle Exploration Problem to Multitask n-Vehicle Exploration Problem (MTNVEP), by combining n-Vehicle Exploration Problem with Job Scheduling Problem. At first, the authors prove that MTNVEP is NP-hard for fixed number of tasks, and it is strongly NP-hard for general number of tasks. Then they propose an improved accurate algorithm with computing time O(n3n), which is better than O(n!) as n becomes sufficiently large. Moreover, four heuristic algorithms are proposed. Effectiveness of the heuristic algorithms is illustrated by experiments at last.