基于CEEMDAN-VSSLMS的滚动轴承故障诊断

针对传统机械轴承故障诊断模型易受系统噪声干扰、特征识别效率低等问题,提出一种基于信号固有模式深度建模分析的轴承故障诊断方法。首先,将采集到的轴承振动信号进行噪声自适应完全经验模态分解(CEEMDAN),获得不同时间尺度的局部特征信号,使用相关系数判别并去除虚假模态分量,再利用可变步长最小均方算法(VSSLMS)对剩余IMF分量降噪并进行重构;然后,将降噪后的振动信号进行离散小波变换(DWT)得到时频谱图,并利用形态学开运算进行特征增强;最后利用改进GoogLeNet网络模型对特征图进行训练,通过Softmax分类器完成特征归类,从而实现轴承故障诊断。将提出的故障诊断方法应用于不同工况下的轴承故障数据集,试验结果表明,所提方法在噪声干扰下具有较高的诊断精度。

基于镜像修正FxLMS控制算法的船舶管路振动主动控制

针对船舶管路减振和抗冲击的需求,本文根据镜像修正自适应滤波算法,设计出了一种管路振动主动控制策略,能够有效地控制管路在低频下的振动,并且在次级通道发生突变时,控制系统可再次快速收敛,进行稳定控制。本文先对镜像修正自适应滤波算法进行理论研究,分析算法的迭代及控制过程;再通过仿真分别验证算法在不同参考信号输入下的收敛性及稳定性;最后搭建实验台架,通过试验验证算法的实际控制效果。试验结果表明:该控制策略在管路振动主动控制中能够降低15.37%的振动强度,比自适应滤波算法控制策略的控制效果好8.85%。所以镜像修正自适应滤波算法能够及时有效地进行管路振动控制。

一种基于动态门限与LMS算法相结合的多径干扰抑制算法

在船舰行驶过程中,信号的传输在岛屿反射与海面散射影响下易产生多径效应,使船舰的无线地空数据接收系统受到影响。为提高接收机的接收性能,首先完成了多径信道的建模,搭建了三径信道模型,并在此模型下,将动态门限法与最小均方(LMS)算法进行改进结合,并用此算法完成了信号处理的研究与仿真。仿真结果表明,所提算法优化方案可以提高在此种情况下信号接收系统的准确性。

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。

高斯曲率与波域LMS算法相结合的图像去噪扩散模型

本文在充分研究传统PM模型的基础上,针对传统模型在模糊边缘细节等信息的不足,先利用图像的几何属性将高斯曲率作为检测算子引入到扩散模型中,将它作为扩散系数来保护边缘控制扩散,从而建立基于高斯曲率的图像去噪模型。考虑到噪声和图像的重要特征都集中在图像的高频部分,再采用小波变换进行小波分解,提取图像的高频部分,在小波域中运用最小均方误差(LMS)算法设计自适应阈值,进一步控制上述新扩散模型的扩散强度,提升去噪效果,建立基于高斯曲率与最小均方误差法的波域PM改进模型,最后将低频部分和经过新模型处理的高频部分进行小波重构,得到最终的去噪图像。实验结果表明,本文方法不仅能够有效去除图像噪声,同时也提升了对重要信息的保护。

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。

Mean-square Almost Periodic Random Functions

In this paper, we present a basic theory of mean-square almost periodicity, apply the theory in random differential equation, and obtain mean-square almost periodic solution of some types stochastic differential equation.

基于LMS自适应滤波器的PMSM谐波抑制方法

为有效抑制电流谐波,在原有的PI控制器基础上,基于最小均方自适应滤波器(LMS-ANF)改造控制器结构,使得永磁同步电机特定次电流谐波得到抑制。通过在传统d,q坐标系矢量控制算法的基础上增加自适应滤波器模块,将采样得到的实际电流作为输入,提取、放大实际电流中的特定次谐波电流,通过PI调节器对谐波误差放大后的电流进行调节,得到控制电压,取得了良好的谐波抑制效果。通过仿真以及台架实验,证明了该控制策略对于抑制电流谐波的有效性。

基于箕舌线可变步长LMS的空频抗干扰算法

针对空频最小均方(Least mean square,LMS)算法抗干扰性能与收敛速度不能兼顾的问题,提出了一种基于箕舌线可变步长LMS的空频抗干扰算法,简称空频基于箕舌线的可变步长LMS算法(Variable step LMS of tongue-like curve function,TLCVSLMS)算法。在兼顾抗干扰性能与收敛速度的基础上,空频TLCVSLMS算法避免了针对每一个频点人为地选取合适的固定迭代步长因子μ的困难,并根据不同频点的信号功率,对箕舌线函数的幅度因子与形状因子作更精细的调节。仿真实验表明,在抗干扰性能接近的情况下,空频TLCVSLMS算法比空频LMS算法少迭代至少400点,空频TLCVSLMS算法的收敛速度更快,而在收敛速度相同的情况下,空频TLCVSLMS算法比空频LMS算法的抗干扰性能提升至少3 dB以上。

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.

A modified fractional least mean square algorithm for chaotic and nonstationary time series prediction

A method of modifying the architecture of fractional least mean square （FLMS） algorithm is presented to work with nonlinear time series prediction. Here we incorporate an adjustable gain parameter in the weight adaptation equation of the original FLMS algorithm and absorb the gamma function in the fractional step size parameter. This approach provides an interesting achievement in the performance of the filter in terms of handling the nonlinear problems with less computational burden by avoiding the evaluation of complex gamma function. We call this new algorithm as the modified fractional least mean square （MFLMS） algorithm. The predictive performance for the nonlinear Mackey glass chaotic time series is observed and evaluated using the classical LMS, FLMS, kernel LMS, and proposed MFLMS adaptive filters. The simulation results for the time series with and without noise confirm the superiority and improvement in the prediction capability of the proposed MFLMS predictor over its counterparts.

LMSF Mean Shift目标跟踪算法

Mean shift作为一种有效的目标跟踪算法近年来得到了广泛的应用,但如何有效地更新核函数直方图模型仍是一个需要解决的问题.本文针对mean shift跟踪算法中模型更新问题,提出了一种新的模型更新策略.对mean shift中目标模型核函数直方图的分量进行自适应LMS滤波(LMS Filter),并且动态更新目标模型的核函数直方图分量,即解决了目标模型更新过快引起的过更新问题,同时核直方图模型又可以及时反映当前目标的颜色、外观变化.本方法通过实验证明取得了较好的跟踪效果.

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.

Revisiting Akaike’s Final Prediction Error and the Generalized Cross Validation Criteria in Regression from the Same Perspective: From Least Squares to Ridge Regression and Smoothing Splines

In regression, despite being both aimed at estimating the Mean Squared Prediction Error (MSPE), Akaike’s Final Prediction Error (FPE) and the Generalized Cross Validation (GCV) selection criteria are usually derived from two quite different perspectives. Here, settling on the most commonly accepted definition of the MSPE as the expectation of the squared prediction error loss, we provide theoretical expressions for it, valid for any linear model (LM) fitter, be it under random or non random designs. Specializing these MSPE expressions for each of them, we are able to derive closed formulas of the MSPE for some of the most popular LM fitters: Ordinary Least Squares (OLS), with or without a full column rank design matrix;Ordinary and Generalized Ridge regression, the latter embedding smoothing splines fitting. For each of these LM fitters, we then deduce a computable estimate of the MSPE which turns out to coincide with Akaike’s FPE. Using a slight variation, we similarly get a class of MSPE estimates coinciding with the classical GCV formula for those same LM fitters.

Degree Splitting of Root Square Mean Graphs

Let be an injective function. For a vertex labeling f, the induced edge labeling is defined by, or;then, the edge labels are distinct and are from . Then f is called a root square mean labeling of G. In this paper, we prove root square mean labeling of some degree splitting graphs.

Mean Square Heun’s Method Convergent for Solving Random Differential Initial Value Problems of First Order

This paper deals with the construction of Heun’s method of random initial value problems. Sufficient conditions for their mean square convergence are established. Main statistical properties of the approximations processes are computed in several illustrative examples.

A Quantized Kernel Least Mean Square Scheme with Entropy-Guided Learning for Intelligent Data Analysis

Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. It can serve as a powerful tool to perform complex computing for network service and application. With the purpose of compressing the input to further improve learning performance, this article proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under the consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the dataset is compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost. The proposed EQ-KLMS is validated using a weather-related dataset, and the results demonstrate the desirable performance of our scheme.

Separate Least Mean Square Based Equalizer with Joint Optimization for Multi-CAP Visible Light Communication

Visible light communication(VLC) is expected to be a potential candidate of the key technologies in the sixth generation(6G) wireless communication system to support Internet of Things(IoT) applications. In this work, a separate least mean square(S-LMS) equalizer is proposed to compensate lowpass frequency response in VLC system. Joint optimization is employed to realize the proposed S-LMS equalizer with pre-part and post-part by introducing Lagrangian. For verification, the performance of VLC system based on multi-band carrier-less amplitude and phase(m-CAP) modulation with S-LMS equalizer is investigated and compared with that without equalizer,with LMS equalizer and with recursive least squares(RLS)-Volterra equalizer. Results indicate the proposed equalizer shows significant improved bit error ratio(BER) performance under the same conditions. Compared to the RLS-Volterra equalizer, SLMS equalizer achieves better performance under low data rate or high signal noise ratio(SNR) conditions with obviously lower computational complexity.

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。

基于SS-LMS算法的自适应DFE均衡电路

针对信道传输对高速串行数据带来的码间串扰问题,提出一种基于符号最小均方根(SS-LMS)算法的半速率判决反馈均衡器(DFE)结构。基于Slicer和积分器电路的结构优化,实现面积和功耗的优化。采用28 nm CMOS工艺实现了连续时间线性均衡器(CTLE)和8抽头DFE组合结构的SerDes电路。测试结果表明,所设计的均衡电路能够将通过38 inch背板传输的16 Gbps信号的眼图水平张开度达到0.56 UI,最大功耗12.25 mW/Gbps。