LMS algorithm for active noise control with improved gradient estimate

Most existed LMS algorithms have noisy gradient estimate since they use instantaneous square error to estimate ensemble average of the square errors. In this paper, a new type of LMS algorithm is developed for active noise control. The gradient estimate in the proposed algorithm is improved by using the weighted time average instead of the instantaneous value to approximate the ensemble average. Compared to the filtered-x LMS algorithm, the proposed algorithm is shown to have many improved convergence properties. Computer simulations are given to support the theoretical analysis.     

RLS and LMS blind adaptive multi-user detection method and comparison in acoustic communication

RLS and LMS blind adaptive multi-user detection algorithm and multi-user detector was proposed to solve the problem of multi-user signal detection problem encountered in underwater acoustic communication networks.In simulation analysis,RLS and the LMS blind adaptive multi-user detector were designed and tested for synchronous and asynchronous multi-user communication process.The results of SIR comparison and MMSE comparison show that,both of the two methods can realize blind adaptive detection when any user change in multi-user communication,during this process,the training communication sequences are not needed.The RLS algorithm has about 5 dB higher in SIR compared with LMS algorithm,and the convergence velocity of RLS algorithm is also higher than LMS algorithm when the communication users change.RLS algorithm has better ability in multi-user detection than that of LMS algorithm,and it has great attraction and guiding significance for solving the problem of multiple access interference(MAI) in multi-user communication.     

一种改进变步长LMS算法及其在系统辨识中的应用

在分析传统LMS算法及变步长LMS的基础上，本文提出了一种改进的变步长LMS算法并应用于系统辨识。新算法利用瞬时误差的四次方和遗忘因子共同来调整步长，进一步解决了收敛时间和稳态误差的矛盾。将新算法应用到系统辨识中时，与传统LMS算法和变步长LMS算法相比，仿真表明新算法有更快的收敛速率。     

基于多尺度小波变换的变步长LMS滤波算法

对LMS自适应算法、基于抽样函数的变步长LMS算法和基于多尺度小波变换的自适应滤波算法进行了研究,在此基础上把变步长LMS算法与多尺度小波变换相结合,产生了新算法。该算法一方面可以克服固定步长LMS算法在收敛速度与收敛精度方面与步长因子的矛盾;另一方面,小波变换的引入减少了输入向量自相关矩阵的条件数,提高了收敛速度、跟踪性能和稳定性。最后对算法的性能进行了计算机仿真比较,仿真结果表明:基于多尺度小波变换的变步长LMS滤波算法具有较快的收敛速度和更强的抑噪能力。     

一种新的LMS算法及其在明渠流量计中的应用

针对变步长LMS(least mean square)自适应滤波算法存在收敛速度慢和稳态误差比较大的问题,根据变步长滤波算法步长调整原理,提出了一种新的基于多项式的非线性变步长LMS算法,通过MATLAB仿真分析研究新算法中各参数对收敛速度和稳态误差的影响,并和已有的变步长LMS算法进行对比,结果表明该算法比已有的算法收敛速度快4倍,且稳态误差小。将新算法应用于明渠流量计回波信号的滤波处理,结果表明在低信噪比下,明渠流量计滤波性能良好,抗干扰能力强。     

小波变换自适应算法的主动振动控制

将小波分析理论和最小均方(least mean square,简称LMS)算法相结合,用分解LMS算法对薄板进行自适应振动控制,并采用了Mallat快速算法,以提高小波自适应算法的实时性。仿真分析和试验结果表明,该方法与传统的LMS算法相比,有效地减小了输入信号自相关矩阵的相关性,且收敛速度和稳定性能均有显著提高。     

Acceleration tracking control combining adaptive control and off-line compensators for six-degree-of-freedom electro-hydraulic shaking tables

An electro-hydraulic shaking table (EHST) is an essential experimental facility to simulate in real-time actual vibration situations. An adaptive controller combined with off-line compensators is proposed to improve the acceleration frequency bandwidth and tracking accuracy of a six-degree-of-freedom (6-DOF) EHST. A servo controller has been employed to implement acceleration closed-loop and coordinate control of the 6-DOF EHST. A recursive extended least-squares algorithm is employed to identify acceleration closed-loop transfer functions and a zero-phase-error tracking controller is used to design off-line inverse model compensators using the identified transfer functions. However, the off-line compensators cannot compensate in real-time varying dynamics of the 6-DOF EHST; so an online adaptive controller with a least-mean-squares (LMS) algorithm based on a delay compensator is employed. The proposed controller combines advantages of the off-line compensators and the online adaptive controller, which guarantees both a fast rate of convergence of the LMS algorithm and high-fidelity acceleration tracking accuracy of the 6-DOF EHST. Some experimental studies have been conducted on a 6-DOF EHST and experimental results show that acceleration tracking control performances, including the rate of convergence of the LMS algorithm and acceleration tracking accuracy, have been improved compared to a conventional three-variable controller and adaptive controllers.     

Adaptive methods for resolution enhancement of ultrasonic NDT signals: Comparative exploration

Adaptive filters, with their efficiency and simplicity, have been used successfully in various ultrasonic NDT signal processing contexts. Of these, the adaptive deconvolution with the conventional least-mean-squares (LMS) adaptive filter has improved time resolution. However; the convergence speed of LMS is restricted by the eigenvalue spread of the input correlation matrix. This paper explores the potential of other adaptive algorithms, namely, normalized least-mean-squares (NLMS), recursive least squares (RLS) and QR-decomposition-based RLS (QR-RLS) to handle the deconvolution of ultrasonic NDT signals and compare their performances with that of the conventional LMS algorithm. Furthermore, the mean square error (MSE) behavior in the different adaptive filtering algorithms for ultrasonic NDT signals deconvolution is briefly introduced. Experiments results are explained by graphs and discussed based on the performance criteria. The proposed methods enhanced the resolution quality, offering more alternatives for this application according to specific case requirements.     

QRS detection using adaptive filters: A comparative study

Electrocardiogram (ECG) is one of the most important physiological signals of human body, which contains important clinical information about the heart. Monitoring of ECG signal is done through QRS detection. In this paper, an improved QRS detection algorithm, based on adaptive filtering principle, has been designed. Enumeration of the effectiveness of various LMS variants used in adaptive filtering based QRS detection algorithm has been done through fidelity parameters like sensitivity and positive predictivity. Whole family of LMS algorithm has been implemented for comparison. Sign-sign LMS, sign error LMS, basic LMS and normalized LMS are re-implemented, while variable leaky LMS, variable step-size LMS, leaky LMS, recursive least squares (RLS), and fractional LMS are novel combination presented in this paper. After analysis of the obtained results, performance of leaky-LMS algorithm is found to be the best with sensitivity, positive predictivity, and processing time of 99.68%, 99.84%, and 0.45 s respectively. Reported results are tested and evaluated over MIT/BIH arrhythmia database. Presented study also concludes that the performance of most of the variants gets affected due to low SNR but the Leaky LMS performs better even under heavy noise conditions.     

Modified-filtered-u LMS algorithm for active noise control and its application to a short acoustic duct

This paper presents a new adaptive algorithm for active noise control (ANC) that can be effectively applicable to a short acoustic duct, such as the intake system of an automobile engine, where the stability and fast convergence of the ANC system is particularly important. The new algorithm, called the modified-filtered-u LMS algorithm (MFU-LMS), is developed based on the recursive filtered-u LMS algorithm (FU-LMS) incorporating the simple hyper-stable adaptive recursive filter (SHARF) to ensure the control stability and the variable step size to enhance the convergence rate. The MFU-LMS algorithm is implemented by purely experimental ways, and is applied to active control of noise in a short acoustic duct, and is validated using two experimental cases of which the primary noise sources are a sinusoidal signal embedded in white noise and a chirp signal. The experimental results demonstrate that the proposed MFU-LMS algorithm gives a considerably better performance than other conventional algorithms, such as the filtered-x LMS (FX-LMS) and the FU-LMS algorithms.     

三种方法在机翼振动主动控制中的仿真研究

针对某型机翼振动响应较大问题,研究了PID、LQR和LMS等3种控制律方法实现振动主动控制技术的可行性。在仿真试验中,采用Patran进行结构建模和压电力等效,通过Matlab的Simulink模块搭建控制仿真模型,使用设计出的试验方案进行。结果表明,通过优化参数,PID、LQR和LMS等3种控制律方法在仿真模拟中均较好地抑制了振动响应,在机翼上实现主动控制技术。LMS方法的控制效果更佳,PID方法的控制收敛时间更短。     

基于 Ｋ- ｍｅａｎｓ信息挥发速率动态调整的 改进蚁群算法

针对蚁群算法在求解 ＴＳＰ问题时,存在容易陷入局部最优,收敛速度慢,且求解时间较长的问 题,提出了一种基于 Ｋ- ｍｅａｎｓ与信息挥发速率动态调整策略的改进蚁群算法,利用 Ｋ- ｍｅａｎｓ聚类算法将 大规模 ＴＳＰ问题分解为数个子问题。在城市选择上,加入轮盘赌规则,对信息素更新规则进行了改进,每轮 迭代时动态调整信息挥发速率。实验表明,相比蚁群算法,改进算法避免了求解陷入局部最优解,加快了算 法的收敛。     

基于IGWO-SVM的大学英语跨文化交际能力评价

为实现大学英语跨文化交际能力的精准评价,改善英语教学对跨文化交际方面的教学困境。本文建立了首先引入最佳点集和随机分布收敛因子改进的GWO算法,并综合运用层次分析法从交际能力和跨文化能力2个方面构建英语跨文化交际能力评价指标体系,最后,大学英语跨文化交际能力评价二级指标的得分数据和能力水平分别作为SVM的输入向量和输出向量,建立了基于IGWO-SVM的大学英语跨文化交际能力评价模型。IGWO-SVM模型的大学英语跨文化交际能力评价具有更高的准确率和更快的收敛速度且具有更高的稳定性,为大学英语跨文化交际能力评价提供了新的方法。     

基于CBR的遗传算法初始种群设计与研究

初始种群的优劣对遗传算法的性能有较大的影响。采用CBR方法产生靠近全局最优解的初始种群,使遗传算法的收敛性和搜索速度得以提高。以电梯群控系统为例,研究了派梯实例的表示及相似度的计算,构建了相应的实例库,并将基于CBR的改进遗传算法用于电梯群控系统中的派梯过程。仿真结果表明,与标准的遗传算法相比,基于CBR的遗传算法具有更高的搜索速度与收敛性,算法的迭代次数减少,同时能使呼梯信号的平均候梯时间降低。     

基于蝙蝠算法的永磁同步电机健康状态监测

永磁同步电机健康状态监测问题可转化为永磁同步电机多参数辨识问题。为提高系统参数辨识及状态监测效率,提出一种基于多智能体蝙蝠算法的永磁同步电机参数辨识方法。多智能体的邻域竞争合作算子实现了蝙蝠个体间的信息交流,提高了全局寻优能力及算法的动态跟踪性能;自学习算子提高算法局部寻优能力,加快算法收敛速度。永磁同步电机多参数辨识结果表明,多智能体蝙蝠算法能快速有效地辨识电机各参数,依据参数变化实现对电机运行状态的监测及预警。与未改进算法相比,验证了改进算法的有效性和优越性能。     

无线传感器网络混合触发一致性时间同步

针对无线传感器节点能量有限、无线通信环境差的特点,将事件触发通信机制应用于无线传感器网络(WSNs)的时间同步中,提出一种混合触发一致性时间同步(MTCTS)算法,并研究同步能耗的降低方法及传输延迟和数据丢包对同步算法的影响。节点的时钟特性建模成二阶状态方程,每个节点通过与其邻居节点交换本地时间这一状态信息获得同步控制输入,从而实现WSNs的时间同步。在所提出的一致性同步算法中,每个节点周期性地更新状态,根据当前状态是否满足给定的事件触发条件去触发通信事件的发生,如此降低通信事件的发生频率。在假定传输延迟服从指数分布和所有节点丢包率相同的情况下,进一步研究了传输延迟、信息丢包对同步精度和算法收敛性的影响。实验和MATLAB仿真验证了所提算法的有效性,并通过仿真确定出了传输延迟和丢包率的上界。     

新的求解钻削路径优化问题算法研究

将粒子群优化算法应用到离散空间的群孔钻削路径优化之中。由于基本粒子群算法不能保证全局或局部收敛,在算法数学模型的基础上,引入重新生成停止进化微粒的方式对算法加以改进,使改进的算法具有全局收敛能力。通过建立序交换元和序交换集对算法的操作算子进行改进,满足钻削路径优化问题中整数编码的需要。实验表明,新的算法具有实现简单,收敛速度快,能够实现全局收敛的优点。     

改进平衡优化器的超声电机无模型自适应控制

为了提高超声电机的控制性能,将基于数据驱动的无模型自适应控制（Model Free Adaptive Control,MFAC）方法应用到超声电机的速度控制中,并针对MFAC存在参数调整困难的问题,提出一种改进的平衡优化器（Improved Equilibrium Optimizer, IEO）算法用于MFAC参数寻优。首先,利用自适应生成概率策略来平衡算法的探索与开发能力;其次,引入折射反向学习策略来扩大解的搜索范围,提高算法的收敛速度,同时采用柯西变异策略来提高算法跳出局部最优的能力;最后,提出一种改进的时间乘以绝对误差积分（Improved Integral Time Absolute Error, IITAE）指标函数用于MFAC的参数寻优。仿真和实验结果表明,与基于原始平衡优化器算法的MFAC相比,基于改进平衡优化器算法的MFAC的稳态误差和调整时间明显减小,系统的控制性能得到显著提高。     

基于改进 NSGA-II 的多无人机三维空间协同航迹规划研究

三维空间多无人机协同航迹规划问题属于多约束条件下的多目标多变量问题,采用 NSGA-II算法进行解决,并对其进行改进。鉴于已知航点的分配问题,利用双染色体编码方式表述航迹;综合考虑无人机自身性能约束以及时间空间协同约束,引入时间空间协同系数表示协同约束情况,并依此改进算法中的选择操作和精英保留策略;同时,提出自适应进化策略,将个体拥挤距离大小与平均个体拥挤距离大小进行对比,并使个体交叉变异概率与迭代次数关联,避免盲目进化,加强算法的搜索能力以及收敛方向的准确性;最后,以航迹长度和受雷达威胁程度作为目标编写代价函数。仿真结果验证了改良后算法的可行性,同时与原算法相比,寻优能力增强,收敛速度及稳定性都有明显提升。     

被动检测系统中自适应算法收敛的动态判断

被动检测应用中，实际环境条件往往比较恶劣而且不可预测，非平稳干扰随时可能发生，如不能准确判断自适应系统实际收敛状况，检测结果将受到严重影响。在实际复杂噪声环境影响下，时间常数已不能代表系统的实际收敛特性，因此提出一种动态判断自适应检测系统迭代算法收敛的方法。基于自适应滤波器工作在理想最优条件时的正交原理，推导出考虑梯度噪声影响时误差与输出信号实际近似正交的约束条件，根据每次迭代不断更新的期望、输出及误差信号的均方估计，在线实时判断收敛并评估自适应解的质量，及时准确停止迭代以得到最佳检测结果，从而避免复杂背景干扰的影响。此动态判断条件对平稳与非平稳环境中的自适应检测均适用，不同条件下的处理结果验证了该方法的有效性。