Noise cancellation of a multi-reference full-wave magnetic resonance sounding signal based on a modified sigmoid variable step size least mean square algorithm

Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.

Active micro-vibration control based on improved variable step size LMS algorithm

The contradiction of variable step size least mean square(LMS)algorithm between fast convergence speed and small steady-state error has always existed.So,a new algorithm based on the combination of logarithmic and symbolic function and step size factor is proposed.It establishes a new updating method of step factor that is related to step factor and error signal.This work makes an analysis from 3 aspects:theoretical analysis,theoretical verification and specific experiments.The experimental results show that the proposed algorithm is superior to other variable step size algorithms in convergence speed and steady-state error.

Novel LMS adaptive filtering algorithm with variable step size

By analyzing algorithms available for variable step size least mean square(LMS)adaptive filter,a new modified LMS adaptive filtering algorithm with variable step size is proposed,along with performance analysis based on different parameters.Compared with the existing algorithms through the simulation,the proposed algorithm has faster convergence speed and smaller steady state error.

Study of Variable Step Size LMS Adaptive Algorithm Based on Variable Region

This paper puts forward a new variable step size LMS adaptive algorithm based on variable region. The step size p（k） in the algorithm varies with the variation of the region of deviation e （k） to ensure the optimization of the three performance objectives including initial convergent speed, trace ability of the time-varying system and steady disregulation. The paper demonstrates the convergence of the algorithm accompanied by random noise,

A novel dynamic step size LMS optimization scheme for interference reducing in FBMC-QAM

Filter bank multicarrier quadrature amplitude modulation(FBMC-QAM)will encounter inter-ference and noise during the process of channel transmission.In order to suppress the interference in the communication system,channel equalization is carried out at the receiver.Given that the con-ventional least mean square(LMS)equilibrium algorithm usually suffer from drawbacks such as the inability to converge quickly in large step sizes and poor stability in small step sizes when searching for optimal weights,in this paper,a design scheme for adaptive equalization with dynamic step size LMS optimization is proposed,which can further improve the convergence and error stability of the algorithm by calling the Sigmoid function and introducing three new parameters to control the range of step size values,adjust the steepness of step size,and reduce steady-state errors in small step sta-ges.Theoretical analysis and simulation results demonstrate that compared with the conventional LMS algorithm and the neural network-based residual deep neural network(Res-DNN)algorithm,the adopted dynamic step size LMS optimization scheme can not only obtain faster convergence speed,but also get smaller error values in the signal recovery process,thereby achieving better bit error rate(BER)performance.

基于sigmoid-sinh分段函数的变步长FxLMS算法

为改善滤波-x最小均方(filtered-x least mean square,FxLMS)算法在噪声主动控制时无法兼顾收敛速度和稳态误差的问题,提出了基于sigmoid-sinh分段函数的FxLMS(SSFxLMS)算法,并引入蚁狮算法对SFxLMS(sigmoid filtered-x least mean square)、ShFxLMS(sinh filtered-x least mean square)、SSFxLMS算法的参数进行优化。分别采用高斯白噪声和实测簇绒地毯织机噪声为输入信号,采用FxLMS、SFxLMS、ShFxLMS、SSFxLMS算法进行噪声主动控制仿真,对比分析这4种算法的性能。结果表明:与其他3种算法相比,采用SSFxLMS算法对高斯白噪声和簇绒地毯织机噪声进行控制时,误差信号的平均绝对值更小,平均降噪量与收敛速度也有大幅度提升。由此可知,SSFxLMS算法有效改善了FxLMS算法无法兼顾收敛速度和稳态误差的问题,研究结果为噪声主动控制算法设计提供了一定的参考。

New normalized LMS adaptive filter with a variable regularization factor

A new normalized least mean square(NLMS) adaptive filter is first derived from a cost function, which incorporates the conventional one of the NLMS with a minimum-disturbance(MD)constraint. A variable regularization factor(RF) is then employed to control the contribution made by the MD constraint in the cost function. Analysis results show that the RF can be taken as a combination of the step size and regularization parameter in the conventional NLMS. This implies that these parameters can be jointly controlled by simply tuning the RF as the proposed algorithm does. It also demonstrates that the RF can accelerate the convergence rate of the proposed algorithm and its optimal value can be obtained by minimizing the squared noise-free posteriori error. A method for automatically determining the value of the RF is also presented, which is free of any prior knowledge of the noise. While simulation results verify the analytical ones, it is also illustrated that the performance of the proposed algorithm is superior to the state-of-art ones in both the steady-state misalignment and the convergence rate. A novel algorithm is proposed to solve some problems. Simulation results show the effectiveness of the proposed algorithm.

Improved Fx-VSSLMS algorithm for active vibration control of smart cantilever beam

Filtered-x least mean square(Fx-LMS) algorithm is popular in many adaptive processes. As its contradiction between convergence speed and stead-state error, the improvements of Fx-LMS algorithm with variable step size(VSS) have been developed. To strengthen the robustness of variable step size least mean square(VSSLMS) algorithms to noise disturbance in active vibration control(AVC) application, nine VSSLMS algorithms are introduced in detail. Then an improved VSSLMS algorithm is proposed for better performance. At last, the performance of these VSSLMS algorithms are compared in AVC experimental system with different noise level. The experimental results verifies the effectiveness and robustness of the proposed VSSLMS algorithm in AVC application.

双曲正切函数的新型变步长LMS算法

针对自适应最小均方误差(Least Mean Square,LMS)滤波算法迭代步长在算法收敛速度、稳态误差间的折中问题,设计了一种基于双曲正切函数的新型变步长算法,算法以双曲正切函数为基础,建立步长因子μ(n)与误差信号e(n)的非线性函数关系,并引入参数α、β和m,设计了一种新的步长调整公式,使得在算法迭代初始阶段采用较大步长因子,达到更快的收敛速度,在接近收敛时采用较小的步长因子,获得更小的稳态误差。通过仿真分析了不同参数对算法性能的影响,与已有典型变步长算法相比,论文算法具有更快的收敛速度、更小的稳态误差和更优的追踪能力。

一种可用于低信噪比环境的变步长LMS算法

论文研究了自适应最小均方误差(Least Mean Squares,LMS)滤波算法的步长选取问题。在分析现有算法的基础上,通过构造步长与误差信号之间的非线性函数,提出一种新的变步长LMS算法。新算法采用误差信号的自相关估计值控制步长,而不是直接利用瞬时误差控制步长,避免了噪声干扰,降低了稳态失调,可工作于低信噪比环境。同时新算法步长控制无记忆效应,提高了收敛速度。仿真表明,新算法的稳态失调和收敛速度均优于现有变步长LMS算法。

基于粒子群优化改进变步长LMS算法的数字基带传输系统

为了减小数字基带传输系统码间串扰,目前主要采用的是最小均方算法自适应滤波,然而由于滤波器阶数以及步长等参数需要根据经验或反复实验比较才可确定出最适合值,会存在系统仅仅在特定环境下才具有较好性能的情况。为此,该文首先通过将粒子群优化算法应用在改进变步长最小均方算法,提出了基于粒子群算法优化的改进变步长最小均方算法。基于此,设计了采用基于粒子群优化改进变步长最小均方算法的数字基带传输系统,其中算法能够根据具体情况进行自适应变化滤波器阶数以及步长,使得系统能够在任何噪声环境下均采用合适的滤波器阶数以及步长从而使误码率降低,获得更优越的性能及更广泛的适用环境。

变步长CMA和DD-LMS双模式切换盲均衡算法

针对经典盲均衡算法收敛速度较慢和稳态误差较大的问题,提出了一种基于变步长恒模算法(Constant Modulus Algorithm, CMA)和判决引导的最小均方(Decision Directed Least Mean Square, DD-LMS)算法的双模式切换盲均衡算法。在算法收敛初期采用CMA算法,以确保算法可以较快收敛。在收敛之后切换至DD-LMS算法,以进一步降低稳态误差。通过设定阈值来切换算法,取相邻多次迭代误差的平均值作为算法的切换值,以确保算法切换时机的合理性。另外,引入Softsign变步长函数并加入3个参数对该函数进行改进,使得Softsign变步长函数可以依据不同信道环境设定最佳参数,同时提高算法的收敛速度。仿真结果表明,在卫星通用信道条件下,所提算法的收敛迭代次数约为1 000次,稳态误差为-12 dB,在信噪比为15 dB时,误码率为1×10~(-6)。与相关算法对比,所提算法的收敛速度较高,误码率和稳态误差较低。

基于变步长凸组合LMS自适应滤波高原动态血氧检测系统设计

针对基于光电检测方法的血氧仪只能在静止状态下测试血氧饱和度(血氧)的问题,提出一种基于变步长凸组合LMS自适应滤波动态血氧检测方法。方法对原始光电描记脉搏波信号经预处理后,通过固步长与变步长LMS自适应算法组合,以三轴加速度计采集信号作为运动噪声参考信号,获得理想脉搏波信号再准确计算血氧值。在此基础上,开发了一套基于物联网的穿戴式高原血氧检测系统,经过模拟正常行走及在不同海拔条件下测试,最大误差不超过4%,验证了算法的有效性和准确性。结果显示,基于物联网的血氧监测系统对于高原旅游中高原反应的实时监测与提醒具有实际意义。

基于改进的双曲正切函数变步长LMS算法

为了改进现有的变步长最小均方误差(least mean square,LMS)算法在低信噪比时性能较差的缺陷,提出了一种基于改进的双曲正切函数的变步长LMS算法,从理论分析和仿真实验两方面讨论了引入参数对算法收敛性、跟踪性、稳定性的影响及算法的抗干扰性。理论分析和仿真实验表明该算法在高低信噪比时均具有较快的收敛速度和跟踪速度以及较小的稳态误差和稳态失调,并且在低信噪比时该算法的收敛性、跟踪性、稳态性均优于其他多种变步长算法。

一种新的变步长LMS自适应滤波算法

本文通过建立步长因子μ与误差信号之间的非线性函数关系,提出了一种新的变步长LMS(LeastMean Square)算法.该算法具有初始阶段和未知系统时变阶段步长自动增大而稳态时步长很小的特点,且克服了S函数变步长LMS算法(简称SVSLMS算法)在自适应稳态阶段μ(n)取值偏大的缺陷.理论分析和计算机仿真结果表明该算法的性能优于SVSLMS算法.

自适应变步长LMS滤波算法及分析

为了提高最小均方(LMS)自适应滤波算法的性能,通过建立步长因子与误差信号之间的非线性函数关系,提出一种自适应变步长LMS算法。该算法具有初始阶段和时变阶段步长自适应增大和稳态阶段步长很小的特点,消除了不相关噪声的影响,并且进一步克服了Sigmoid函数变步长LMS算法在自适应稳态阶段步长取值偏大的缺陷,计算机仿真结果与理论分析相一致,证实该算法优于传统算法。

一种改进的解相关LMS自适应算法

针对变步长LMS自适应滤波算法在输入信号高度相关时,收敛速度下降导致性能下降的问题,提出了一种改进的解相关LMS自适应算法,该算法引入解相关原理和归一化处理,用输入向量的正交分量来更新滤波器权系数,有效加快了算法的收敛速度,且稳态误差小,使得算法在有色输入和大范围的动态输入下都能保持良好性能.

LMS和归一化LMS算法收敛门限与步长的确定

从 LMS算法失调量的准确表达式出发,根据输入信号特征值分布重新研究了 LMS,归一化LMS(Normalized LMS,NLMS)算法收敛的必要条件,推导出 LMS和 NLMS 算法收敛的步长门限,并分析了输入信号特征值分布、滤波器阶数对算法收敛步长门限的影响,推导出满足性能失调下步长的自适应计算公式,减小了应用 LMS,NLMS算法时步长选取的盲目性,与已有的算法相比,具有计算简单、实用、自适应性能强,同时可获得满意失调量的特点,计算机模拟结果表明该方法的正确性。

基于指数函数的归一化变步长LMS算法

在研究归一化最小均方误差(NLMS)算法的基础上,提出一种基于指数函数的变步长LMS算法。通过建立误差和步长的函数关系,实时调整步长,并对输入信号完成时域信号解相关,解决稳态失调系数与收敛速度的矛盾。仿真实验结果证明,该算法与传统LMS算法、SVS_LMS算法、NLMS算法以及双曲正切变步长LMS算法相比,具有更高的收敛速度和较小的稳态失调系数。

一种变步长LMS自适应滤波算法及分析

本文对变步长自适应滤波算法进行了讨论 ,建立了步长因子 μ与误差信号e(n)之间另一种新的非线性函数关系 .该函数比已有的Sigmoid函数简单 ,且在误差e(n)接近零处具有缓慢变化的特性 ,克服了Sigmoid函数在自适应稳态阶段步长调整过程中的不足 .由此函数本文得出了另一种新的变步长自适应滤波算法 ,并且分析了参数α ,β的取值原则及对算法收敛性能的影响 .该算法有较好的收敛性能且计算量少 .计算机仿真结果与理论分析相一致 。