Thefilter-x least mean square(FxLMS)algorithm is widely used in active noise control(ANC)systems.However,because the algorithm is a feedback control algorithm based on the minimization of the error signal variance to ...Thefilter-x least mean square(FxLMS)algorithm is widely used in active noise control(ANC)systems.However,because the algorithm is a feedback control algorithm based on the minimization of the error signal variance to update thefilter coefficients,it has a certain delay,usually has a slow convergence speed,and the system response time is long and easily affected by the learning rate leading to the lack of system stability,which often fails to achieve the desired control effect in practice.In this paper,we propose an active control algorithm with near-est-neighbor trap structure and neural network feedback mechanism to reduce the coefficient update time of the FxLMS algorithm and use the neural network feedback mechanism to realize the parameter update,which is called NNR-BPFxLMS algorithm.In the paper,the schematic diagram of the feedback control is given,and the performance of the algorithm is analyzed.Under various noise conditions,it is shown by simulation and experiment that the NNR-BPFxLMS algorithm has the following three advantages:in terms of performance,it has higher noise reduction under the same number of sampling points,i.e.,it has faster convergence speed,and by computer simulation and sound pipe experiment,for simple ideal line spectrum noise,compared with the convergence speed of NNR-BPFxLMS is improved by more than 95%compared with FxLMS algorithm,and the convergence speed of real noise is also improved by more than 70%.In terms of stability,NNR-BPFxLMS is insensitive to step size changes.In terms of tracking performance,its algorithm responds quickly to sudden changes in the noise spectrum and can cope with the complex control requirements of sudden changes in the noise spectrum.展开更多
针对网络控制系统中采样周期时变不确定性对控制性能和网络运行性能的影响,提出一种基于反馈控制原理和预测机理的智能动态调度策略。该策略利用网络资源利用率、截止期错过率以及误差绝对值积分(Integral of the Absolute Error,IAE)...针对网络控制系统中采样周期时变不确定性对控制性能和网络运行性能的影响,提出一种基于反馈控制原理和预测机理的智能动态调度策略。该策略利用网络资源利用率、截止期错过率以及误差绝对值积分(Integral of the Absolute Error,IAE)对消息进行反馈控制调度,保证网络利用率、截止期错过率以及控制性能保持在期望的范围内;利用BP神经网络对网络利用率和数据包执行时间进行预测,实时调整控制系统的采样周期,以适应网络中信息流的变化。仿真试验结果表明该调度算法既能满足控制系统的性能,又提高网络资源的利用率。展开更多
基金This work was supported by the National Key R&D Program of China(Grant No.2020YFA040070).
文摘Thefilter-x least mean square(FxLMS)algorithm is widely used in active noise control(ANC)systems.However,because the algorithm is a feedback control algorithm based on the minimization of the error signal variance to update thefilter coefficients,it has a certain delay,usually has a slow convergence speed,and the system response time is long and easily affected by the learning rate leading to the lack of system stability,which often fails to achieve the desired control effect in practice.In this paper,we propose an active control algorithm with near-est-neighbor trap structure and neural network feedback mechanism to reduce the coefficient update time of the FxLMS algorithm and use the neural network feedback mechanism to realize the parameter update,which is called NNR-BPFxLMS algorithm.In the paper,the schematic diagram of the feedback control is given,and the performance of the algorithm is analyzed.Under various noise conditions,it is shown by simulation and experiment that the NNR-BPFxLMS algorithm has the following three advantages:in terms of performance,it has higher noise reduction under the same number of sampling points,i.e.,it has faster convergence speed,and by computer simulation and sound pipe experiment,for simple ideal line spectrum noise,compared with the convergence speed of NNR-BPFxLMS is improved by more than 95%compared with FxLMS algorithm,and the convergence speed of real noise is also improved by more than 70%.In terms of stability,NNR-BPFxLMS is insensitive to step size changes.In terms of tracking performance,its algorithm responds quickly to sudden changes in the noise spectrum and can cope with the complex control requirements of sudden changes in the noise spectrum.
文摘针对网络控制系统中采样周期时变不确定性对控制性能和网络运行性能的影响,提出一种基于反馈控制原理和预测机理的智能动态调度策略。该策略利用网络资源利用率、截止期错过率以及误差绝对值积分(Integral of the Absolute Error,IAE)对消息进行反馈控制调度,保证网络利用率、截止期错过率以及控制性能保持在期望的范围内;利用BP神经网络对网络利用率和数据包执行时间进行预测,实时调整控制系统的采样周期,以适应网络中信息流的变化。仿真试验结果表明该调度算法既能满足控制系统的性能,又提高网络资源的利用率。
