基于LMS算法自适应噪声抵消系统的仿真研究

介绍了自适应噪声抵消系统的原理和LMS算法,并给出了基于LMS算法自适应噪声抵消系统的仿真图。通过分析仿真结果表明基于LMS算法的自适应噪声抵消技术可以有效的抵消正弦干扰信号,这样在语音信号处理中就能消除含噪语音信号中的背景噪音,达到提高语音信号质量的目的。     

基于LMS算法的自适应滤波器在海浪磁场噪声中的应用

针对海浪产生的磁场信号对海洋微弱磁信号检测影响较大的问题,利用Weaver理论建立海浪产生电磁场噪声模型作为研究对象,并通过基于最小均方（LMS）算法的自适应滤波器对探测磁场进行处理,能有效滤除海浪磁噪声,检测出微弱磁信号,达到了滤除海浪磁场噪声的目的。     

基于FPGA的分布式算法FIR滤波器设计

FIR滤波器具有许多优点,是数字信号处理系统中基本的元件。本文比较了目前FIR滤波器硬件实现的几种方法,详细研究了基于FPGA、采用分布式算法实现FIR滤波器的原理和方法,设计了一个32阶线性相位FIR滤波器,并用VHDL语言对其进行了描述。此滤波器采用串行加法器将数据进行预相加,从而将滤波器的规模减半。其主要部分——乘累加单元,采用LUT查找表结构,将乘法运算转换为查表操作,提升处理速度。最后进行了硬件仿真,结果证明,这一方法是可行且高效的。     

An easy parameter estimation by the EM algorithm in the newup-and-down method

Flashover voltage estimation has been carried out conventionally with the up-and-down method by Dixon and Mood. Recently, Hirose and Kato proposed a new version of the method to change the way of analyzing data. Although the method has better properties than the conventional one, it requires solving maximum likelihood equations. In this paper we reduce the troublesome task (e.g. implementation and the selection of a proper initial value) attendant on the requirement by using the expectation-maximization (EM) algorithm that gives a useful iterative formula to solve the equations. The iterative formula almost always can give the solutions of the likelihood equations because of its excellent global convergence of the EM algorithm     

基于变步长LMS自适应陷波算法的电气信号频率测量

提出一种基于二阶无限冲击响应(IIR)变步长自适应数字陷波滤波器的电气信号频率跟踪测量算法。论述了陷波滤波器能够滤除信号中特定的频率,而其他的频率成分不受影响的原理;最小均方LMS(LeastMeanSquare)自适应陷波算法是将被谐波和随机噪声污染的电气信号通过基波陷波器,根据陷波器输出误差采用变步长因子的递推LMS自适应修正陷波器参数和跟踪频率的变化。实例中,给出了给定变步长迭代公式的常数以计算出频率,并采用频率稳定时的测量、频率波动时的测量、电机运行频率测量的3种仿真结果表明所提出的频率跟踪测量算法效果良好。     

基于LMS算法的自适应滤波在负压波信号噪声处理中的应用

本文以负压波信号采集滤波为例,介绍了基于LMS算法的自适应滤波器的基本原理和该滤波器几个关键参数设定的基本方法。实践证明,在采集随机不确定信号时,采用此滤波器进行信号调理具有比传统滤波器不可比拟的优越性。     

A fast algorithm for the weighted least absolute value stateestimation [for power systems]

The weighted least absolute value minimization method is used to solve the power system state estimation problem. The problem can be shown to be equivalent to a linear programming program and hence can be solved using the well-known Simplex method. However, a direct adaptation of the Simplex method results in an inefficient code. A fast algorithm originally suggested by I. Barrodale and F.D.K. Roberts (1973) is further improved and applied to the power system state estimation problem. The use of the algorithm resulted in considerable savings in computation times, as demonstrated by the numerical examples given for various test systems     

Waiting in line [queuing theory]

Queuing phenomenon encompasses all activities of our lives. We have to wait in line whenever the number of servers or the service rate of the server does not match the rate at which the customers arrive in the queue. Queuing theory is the formal analysis of this phenomenon in search of finding the optimum solution to this problem so that everybody gets service without waiting for a long time in line. After giving an introduction to queuing theory, the author discusses real world applications of the theory     

FIR数字滤波器的优化与验证

引入数字滤波器的基本概念和其在工程应用中的作用,阐述FIR数字滤波器的基本结构和设计原理,介绍优化的各种算法,通过VHDL硬件描述语言实现,并利用FPGA对不同的优化设计实例给予验证,仿真。判断仿真验证优化的可靠性,提交综合报告,进一步分析造成优化差异的本质原因,根据优化的特点选择不同工程背景下的滤波器设计优化方法。     

有限长脉冲响应滤波器在视频格式转换芯片中的应用

本文提出了一种高性能有限脉冲响应(FIR)滤波器结构,用于视频格式转换芯片中尺寸缩放模块算法的实现,该结构采用流水线技术1,同时结合了并行性、系数对称性、“0”的特性和乘法优化(加法代替);阐述了该FIR滤波器的设计原则和优化方法,给出了该FIR滤波器FPGA实现的框图和综合结果,与传统结构实现的FIR滤波器比较具有更高的性能和兼容性。     

FIR数字低通滤波器工频干扰抑制能力的提高

用Matlab按窗函数法设计较低阶数的FIR数字低通滤波器时,其工频干扰抑制能力一般需要优化。通过对其幅频特性的研究,发现适当调整截止频率可显著提高其工频干扰抑制能力。设计了Matlab程序,用户只需输入一些基本要求,即可获得具有高工频干扰抑制能力的FIR数字低通滤波器。     

The Kalman filter in the context of adaptive filter theory

Model‐based adaptive algorithms are usually derived with the help of the Wiener‐Hopf equation based on empirical statistics. They are often interpreted as an extension to their model‐independent counterparts, i.e. the stochastic‐gradient based adaptive filters. As a consequence, it is generally not considered worthwhile to show the analogy between Kalman filters and adaptive filters. This article pursues just these two goals. First, it tries to remove the notion that the Kalman filter is a complicated and unnecessary detour from the subject of adaptive filtering. Second, the advantage of a deeper insight into adaptive algorithms from Kalman's viewpoint emerges from our treatment. Based on a time‐varying FIR filter model, the Kalman filter is completely derived and serves as a general framework for the special case of model‐based adaptive filters. Copyright © 2004 John Wiley & Sons, Ltd.     

FIR数字滤波器在风机振动系统中的应用

本文介绍了FIR数字滤波器的原理、窗函数设计法及应用.使用海明窗设计FIR带通滤波器,并应用在风机振动监测系统中.首先在MATLAB上仿真滤波器的设计,并模拟滤波过程,评估滤波效果;然后将FIR带通滤波器应用到实际风机振动系统中.根据实际系统的特点,利用快速卷积和重叠保留法,保证了数字滤波器处理过程的实时性和快速性,并节省了硬件资源消耗.实验结果证明,设计的滤波器性能良好,对正确分析风机振动情况有着较高的应用价值.     

改进窗函数在FIR数字滤波器设计中的应用

电网谐波分析已成为电力系统环境治理的重要课题,为了提高谐波分析的精度,对采样得到的信号进行滤波处理是必不可少的环节.窗函数法是FIR数字滤波器设计中最简单的方法,正确地选择窗函数可以提高所设计的数字滤波器的性能.通过对Gauss窗函数的改进,使设计出的低通滤波器具有更好的优越性,并通过Matlab仿真验证了该方法的有效性.     

基于多目标系统模糊优选理论的冷媒管道泵最佳转速优选

介绍了微型啤酒生产线计算机控制系统的硬件配置和软件设计。温度是啤酒发酵过程的关键参数 ,在该生产线中通过变频器驱动管道泵使冷媒在发酵罐中强制循环来控制温度 ,于是管道泵转速的确定就成为关键问题。依据多目标系统模糊优选理论提出一种管道泵转速的优选策略 ,综合考虑了各种指标 ,满足了工艺需要。     

最小均方差算法在智能天线非盲波束形成中的应用

智能天线是当前通信领域的研究热点之一,智能天线研究的核心内容是自适应波束形成算法.本文阐述了智能天线的基本原理,详细介绍了最小均方差算法以及它在智能天线非盲波束形成中的应用.文章以四元均匀线阵为例,利用计算机模拟仿真给出了最小均方差算法的性能分析,并讨论了实验参数对算法性能的影响.实验结果表明,天线方向图的主瓣能准确地指向目标用户的来波方向和主动抑制干扰信号;迭代步长的增大对算法的收敛速度有明显提高;而信干比的提高对于算法收敛速度的影响很小.     

LMS自适应滤波器在雁淮特高压直流工程交流滤波器保护系统中的应用研究

以雁淮直流输电工程交流滤波器保护系统的不平衡电流校正为研究对象,介绍了C1高压电容器组的结构和不平衡保护的原理。阐明了不平衡电流校正的重要性和意义,指出传统方法所使用的初始不平衡电流不充分考虑其相位和幅值的波动,将其作为恒定值是不合理的。针对初始不平衡电流的随机性,介绍了LMS自适应滤波器的原理和特性,并将其在HCM3000平台中进行了实现。研究并给出了滤波器关键参数,阐述了在具体工程应用中的使用方法。工程数据验证了设计的合理性和性能的优越性。     

A simple demonstration of numerical dispersion under FDTD [for EMfield calculations]

The method of finite differences in the time domain (FDTD) has become of growing importance for solving electromagnetic problems due to its simplicity, versatility and the available of inexpensive and powerful computers. In this work, the authors try to demonstrate in an understandable way the characteristic of numerical dispersion of the algorithm. For this purpose, they simulate the one-dimensional propagation of different wave shapes under FDTD. In order to enhance the fact that the dispersion arises as a consequence of the different phase and group velocities for monochromatic waves, they decompose the signals into spectral components and, after propagation at the phase speed given by FDTD, they reconstruct the signals. This computation gives the same results as FDTD. Finally, they compute the propagation assuming that the phase speed its truly the speed in vacuum. In this case, no dispersion its observed at all