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 μ(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.

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.

An Effective Runge-Kutta Optimizer Based on Adaptive Population Size and Search Step Size

A newly proposed competent population-based optimization algorithm called RUN,which uses the principle of slope variations calculated by applying the Runge Kutta method as the key search mechanism,has gained wider interest in solving optimization problems.However,in high-dimensional problems,the search capabilities,convergence speed,and runtime of RUN deteriorate.This work aims at filling this gap by proposing an improved variant of the RUN algorithm called the Adaptive-RUN.Population size plays a vital role in both runtime efficiency and optimization effectiveness of metaheuristic algorithms.Unlike the original RUN where population size is fixed throughout the search process,Adaptive-RUN automatically adjusts population size according to two population size adaptation techniques,which are linear staircase reduction and iterative halving,during the search process to achieve a good balance between exploration and exploitation characteristics.In addition,the proposed methodology employs an adaptive search step size technique to determine a better solution in the early stages of evolution to improve the solution quality,fitness,and convergence speed of the original RUN.Adaptive-RUN performance is analyzed over 23 IEEE CEC-2017 benchmark functions for two cases,where the first one applies linear staircase reduction with adaptive search step size(LSRUN),and the second one applies iterative halving with adaptive search step size(HRUN),with the original RUN.To promote green computing,the carbon footprint metric is included in the performance evaluation in addition to runtime and fitness.Simulation results based on the Friedman andWilcoxon tests revealed that Adaptive-RUN can produce high-quality solutions with lower runtime and carbon footprint values as compared to the original RUN and three recent metaheuristics.Therefore,with its higher computation efficiency,Adaptive-RUN is a much more favorable choice as compared to RUN in time stringent applications.

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.

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.

Joint receiving mechanism based on blind equalization with variable step size for M-QAM modulation

The problem of inter symbol interference（ ISI） in wireless communication systems caused by multipath propagation when using high order modulation like M-Q AMis solved. Since the wireless receiver doesn＇t require a training sequence,a blind equalization channel is implemented in the receiver to increase the throughput of the system. To improve the performances of both the blind equalizer and the system,a joint receiving mechanismincluding variable step size（ VSS） modified constant modulus algorithms（ MC-MA） and modified decision directed modulus algorithms（ MD DMA） is proposed to ameliorate the convergence speed and mean square error（ MSE） performance and combat the phase error when using high order QAM modulation. The VSS scheme is based on the selection of step size according to the distance between the output of the equalizer and the desired output in the constellation plane. Analysis and simulations showthat the performance of the proposed VSS-MCMA-MD DMA mechanismis better than that of algorithms with a fixed step size. In addition,the MCMA-MDDMA with VSS can performthe phase recovery by itself.

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。

一种用于毫米波探测系统的变步长LMS-AGC方案

在毫米波探测系统中,自动增益控制(AGC)是信号处理系统的关键技术之一,其作用是应对大动态范围的中频信号接收。本文阐述了一种数字自动增益控制(DAGC)的实现方案,通过对中频信号的数字采样进行处理,采用基于平均功率参量的变步长LMS-AGC自适应算法,将自适应滤波器的变步长思想应用到AGC方案中,并对其步长算法进行优化。通过对比普通功率检测自动增益控制和固定步长LMS-AGC方案,本文提出的变步长LMS-AGC方案具有更快的收敛速度和更小的稳态误差。

箕舌线变步长LMS/F算法数字域自干扰消除研究

自干扰消除技术是实现带内全双工(IBFD)通信的重要前提,其中数字域自干扰消除是带内全双工通信系统中硬件复杂度最低、灵活性最高的自干扰消除技术,并且是自干扰消除的最后一道防线。然而,其消除能力仍需提升,主要是如何处理收敛速度和稳态精度之间的平衡,并且还要具备突变信道的自适应能力。文章提出了一种新的全双工系统的数字自干扰消除方法,发射链路采用数字、模拟预失真技术消除功率放大器的非线性失真,使用辅助接收链获取发射链路信号副本,在数字域中利用重构自干扰信号副本消除接收信号残余自干扰信号和功率放大器残余非线性失真,并通过在接收链与辅助接收链之间共用一个振荡器消除部分接收机相位噪声。仿真表明,该方法与已有变步长LMS消除方法相比,在信噪比为5 dB的条件下,能够在提高收敛速度的同时获得优于变步长LMS方法的消除能力。

基于对称非线性函数的变步长LMS自适应滤波算法

为解决自适应最小均方误差(least mean squares,LMS)滤波算法难以平衡稳态误差和收敛速度的问题,提出了基于对称非线性函数的变步长LMS自适应滤波算法。通过自变量取绝对值、叠加非线性拉伸量改进Sig-moid函数,构造一个对称非线性函数用于刻画步长因子与稳态误差的非线性关系。该对称非线性函数具有能够根据误差动态调整步长、更快达到收敛状态的特点。根据构造的对称非线性函数和输入信号功率生成归一化变步长因子,解决噪声逐级放大的问题,进一步提高算法的滤波效果同时,加速收敛。实验表明:该算法在低信噪比、信噪比变化、信号频率变化、滤波器阶数变化、延迟采样点数变化条件下均具有更好的滤波效果、更优的稳定性和更快的收敛速度。

一种混合多策略改进的麻雀搜索算法

针对麻雀搜索算法SSA求解目标函数最优解时具有过早收敛、在多峰条件下易陷入局部最优和在高维情况下求解精度不足等问题,提出了一种混合多策略改进的麻雀搜索算法MISSA。考虑到算法初始解的质量很大程度上会影响整个算法的收敛速度与精度,引入精英反向学习策略,扩大算法的搜索区域,提升初始种群的质量和多样性;对步长进行分阶段控制,以提高算法的求解精度;通过在跟随者的位置中加入Circle映射参数与余弦因子,提高算法的遍历性与搜索能力;采用自适应选择机制在麻雀个体位置更新中加入Lévy飞行,增强算法寻优和跳出局部最优的能力。将改进后的算法与麻雀搜索算法及其他算法在13个测试函数上进行对比,并进行Friedman检验。实验结果表明,改进后的麻雀搜索算法能够有效提高寻优精度与收敛速度,并在高维问题中也具备较高的稳定性。

基于双曲正弦函数设计变步长的LMS算法

针对非高斯环境下传统变步长LMS(Variable step-size least mean square,VSS-LMS)算法性能不佳的问题,基于传统的VSS-LMS算法利用双曲正弦函数构建变步长的更新策略,提出一种基于双曲正弦函数的变步长LMS算法。并在理论上分析了新提出VSS-LMS算法的收敛性与算法复杂度,并给出在不同输入信号时对两种特性的线性系统的VSS-LMS算法的辨识结果,且每次仿真中都在不同分布的非高斯噪声下进行。结果表明,提出的算法相比Log-NLMS算法和改进G-SVSLMS算法,新提出的VSS-LMS算法具有更快的收敛速度和较好的稳态特性,且稳态误差趋于理论的SNR。

信号插值对归一化变步长FxLMS算法的影响研究

自适应滤波算法是主动控制系统的核心部分,为进一步优化归一化变步长滤波x-最小均方(NVSS-FxLMS)算法,本研究引入信号插值概念,构建结合信号插值与归一化变步长理念的INVSS-FxLMS算法,以进一步改善控制效果及算法收敛速度,并探究了信号插值数量对算法性能的影响。仿真结果表明,信号插值可改善NVSS-FxLMS算法的控制效果及收敛速度,且插值数量存在最佳范围,当插值数量为10左右时,控制效果及收敛速度较好,总声压级/加速度级降低率达18%,而计算时间仅增加约9.6%,为NVSS-FxLMS算法的发展提供了新思路,并为主动控制系统的改进奠定基础。

基于动态模态分解-自适应变步长油浸式电力变压器绕组瞬态温升快速计算方法

为了改善当前油浸式电力变压器绕组瞬态温升计算过慢的问题,该文结合动态模态分解法和自适应变步长法提出了一种动态模态分解(DMD)-自适应变步长(ATS)快速计算方法。首先,该方法引入了动态模态分解算法,利用动态系统中前若干时步提取得到的变化特征,近似拟合其后一段时间内的系统变化,并通过选取主模态降低计算时间。其次,为了提高DMD算法性能,进一步提出结合ATS方法,通过自适应调整计算步长,提高瞬态计算效率;为了验证算法的计算精度和效率,建立了八分区分匝绕组数值传热模型并在此基础上将该文所提算法与仿真软件Fluent的计算结果进行对比,结果表明,在计算精度方面,二者结果几乎一致,计算误差最大不超过0.3 K;对于计算效率,该文算法的总计算时间为5.99 s,仅为Fluent总计算时间的1/89,且算法时间步数仅为Fluent的4.7%。最后,为了验证DMD-ATS算法的工程实用性,基于产品级油浸式电力变压器绕组结构搭建温升实验平台,并将所提算法计算结果与实验结果进行对比,结果表明所提算法在各测量线饼的误差均处在可接受的范围内,最大误差仅为4.57 K,且包含预处理时间在内的计算时间仅为69.14 s,计算时步仅需17步,较当前主流的计算方法效率有明显提高。综合算法的精度和效率,充分说明所提算法具有一定工程价值。

融合三维螺旋运动和混合反向学习策略的改进鹈鹕优化算法

针对鹈鹕优化算法收敛速度较慢、初始化过程随机产生初始种群导致种群多样性差,在后期易陷入局部最优等问题,提出了一种融合三维螺旋飞行和混合反向学习策略的鹈鹕优化算法。首先使用Gauss映射初始化种群,提高种群多样性;其次利用三维螺旋飞行和混合最优最差反向学习策略,加强算法跳出局部最优的能力;最后,引入自适应平衡因子与自适应步长,提出鹈鹕坠落策略,以模拟捕食过程中群体的微小变化。最后,通过12个基准函数和实际案例对IPOA(improved pelican optimization algorithm)进行测试,并与8个仿生算法进行对比,测试结果与Wilcoxon符号秩和检验结果均表明IPOA收敛精度与稳定性等各项性能都有所提升,具有明显优势。

基于POD-αATS的油浸变压器瞬态温升降阶自适应变步长计算方法

针对油浸式电力变压器瞬态温升计算效率过低的问题,该文提出本征正交分解-αATS(proper orthogonal decomposition-adaptive time stepping based onαfactor,POD-αATS)降阶自适应变步长瞬态计算方法。首先,推导变压器绕组瞬态温升计算的有限元离散方程;其次,采用POD降阶算法改善传统瞬态计算中存在的条件数过大及方程阶数过高的问题;同时对于瞬态计算中的时间步长选择问题,提出适用于非线性问题的αATS变步长策略;然后,为验证方法的有效性,基于110 kV油浸式电力变压器绕组的基本结构建立二维八分区数值计算模型,同时将计算结果与基于110 kV绕组的温升实验结果进行对比。数值计算及实验结果表明,所提算法与全阶定步长算法在流场和温度场中的精度几乎相同,且流场计算效率提升约45倍,温度场计算效率提升约38倍,计算速度得到显著提高。这一点在温升实验中同样得到验证,说明该文所提算法的准确性、高效性及一定的工程实用性。

柔性机械爪力控的变步长自适应滤波算法

机械爪工作中,由于电源和机械震动会导致电流有波动,将测得的电流数据通过滤波,减少电流的波动,从而让力控的准确度更高。传统的自适应算法无法满足在快速收敛的同时,实现较高鲁棒性和较小稳态误差的要求。针对上述问题,文中提出一种新的自适应滤波算法。首先,建立基于双曲正切函数的变步长模型,实现更新步长因子的动态变值,其次,将滤波器权向量差及可变因子引入更新权值迭代算法中,以达到快速收敛和减少稳态误差的目的。通过实验证明,文中算法实现了快速收敛,并具有较小的稳态误差,提高了系统的鲁棒性。

Optimal variable step-size LMS model and algorithm with independence assumption

To solve the contradiction between convergence rate and steady-state error in least mean square (LMS) algorithm, basing on independence assumption, this paper proposes and proves the optimal step-size theorem from the view of minimizing mean squared error (MSE). The theorem reveals the one-to-one mapping between the optimal step-size and MSE. Following the theorem, optimal variable step-size LMS (OVS-LMS) model, describing the theoretical bound of the convergence rate of LMS algorithm, is constructed. Then we discuss the selection of initial optimal step-size and updating of optimal step-size at the time of unknown system changing. At last an optimal step-size LMS algorithm is proposed and tested in various environments. Simulation results show the proposed algorithm is very close to the theoretical bound.