Description: By building a nonlinear function relationship between an d the error signal，this paper presents a no—
vel variable step size LMS(Least Mean Square)adaptive filtering algorithm． Platform: |
Size: 2944 |
Author:上将 |
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Description: 此程序可实现基于LMS（最小均方误差算法）的自适应滤波程序-this procedure can be based on the LMS (least-mean-square error algorithm) adaptive filtering process Platform: |
Size: 1024 |
Author:尚云超 |
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Description: By building a nonlinear function relationship between an d the error signal，this paper presents a no—
vel variable step size LMS(Least Mean Square)adaptive filtering algorithm．-By building a nonlinear function relationship between an d the error signal, this paper presents a no-vel variable step size LMS (Least Mean Square) adaptive filtering algorithm. Platform: |
Size: 3072 |
Author:上将 |
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Description: 一些数学处理函数，包括：用频域最小误差平方方法设计线性相位FIR低通数字滤波器、最小均方(LMS)算法的自适应数字滤波、离散小波变化函数-Deal with some mathematical functions, including: use of frequency domain methods of design of the smallest square error linear phase FIR low-pass digital filter, least mean square (LMS) algorithm for adaptive digital filtering, discrete wavelet function changes Platform: |
Size: 3072 |
Author:chenjie |
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Description: 这个包中包含学习最小均方滤波的一个例子及其和其它滤波方法的一些比较。-This package contains the learning of least mean square with an example. And it compared least mean square method with other filtering methods. Platform: |
Size: 2048 |
Author:桂林 |
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Description: 自适应滤波算法是自适应滤波器实现过程中较为重要的环节,数字信号处理器的出现为数字信号处理算法的
实现和大规模数据的实时处理提供了可能。通过对自适应最小均方算法(LMS) 及其各种改进算法的Matlab 仿真,进行分
析及归纳比较,得出结论,并在此基础上,提出算法的优化方案,以DSP 为平台,用汇编语言对自适应算法进行了描述,最终
以DSP 为平台完成了自适应滤波器的设计。-Adaptive filtering algorithm is an adaptive filter process is more important part of the emergence of digital signal processor for digital signal processing algorithm implementation and large-scale real-time data processing possible. The adaptive least mean square (LMS) algorithm and its various improved Matlab simulation, we analyze the comparison, draw conclusions, and on this basis, the proposed algorithm optimization program to DSP platform, with the assembly Language of the adaptive algorithm is described, the final completion of DSP-platform for adaptive filter design. Platform: |
Size: 360448 |
Author:xiliao |
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Description: This book presents a comprehensive and unifying introduction to kernel adaptive fi ltering. Adaptive signal processing theory has been built on three pillars: the linear model, the mean square cost, and the adaptive least - square learning algorithm. When nonlinear models are required, the simplicity of linear adaptive fi lters evaporates and a designer has to deal with function approximation, neural networks, local minima, regularization, and so on. Is this the only way to go beyond the linear solution? Perhaps there is an alternative, which is the focus of this book. The basic concept is to perform adaptive fi ltering in a linear space that is related nonlinearly to the original input space. If this is possible, then all three pillars and our intuition about linear models can still be of use, and we end up implementing nonlinear fi lters in the input space. - This book presents a comprehensive and unifying introduction to kernel adaptive fi ltering. Adaptive signal processing theory has been built on three pillars: the linear model, the mean square cost, and the adaptive least - square learning algorithm. When nonlinear models are required, the simplicity of linear adaptive fi lters evaporates and a designer has to deal with function approximation, neural networks, local minima, regularization, and so on. Is this the only way to go beyond the linear solution? Perhaps there is an alternative, which is the focus of this book. The basic concept is to perform adaptive fi ltering in a linear space that is related nonlinearly to the original input space. If this is possible, then all three pillars and our intuition about linear models can still be of use, and we end up implementing nonlinear fi lters in the input space. Platform: |
Size: 1442816 |
Author:johnny |
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Description: 自适应滤波中的最小均方算法（LMS）的改进型归一化LMS算法的仿真程序。-least mean square (LMS) algorithm inAdaptive filtering，modified owned by an LMS algorithm for the simulation program .
Platform: |
Size: 1024 |
Author:饭饭 |
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Description: 基于LMS(最小均方误差算法)的自适应滤波的源程序序，基于matlab ，经测试可直接使用。
-Based on the LMS (least mean square error algorithm) adaptive filtering of the source sequence, based on Matlab, has been tested and can be used directly. Platform: |
Size: 1024 |
Author:察觉 |
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Description: LMS最小均方算法,作为自适应滤波以及自适应均衡过程的一个评估准则-LMS least mean square algorithm as an adaptive filtering and adaptive equalization process assessment criteria Platform: |
Size: 1024 |
Author:于海龙 |
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Description: 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L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(Least
Mean 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L(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.
-By building a nonlinear function relationship betweenLand the error signal,this paper
presents a novel variable step size LMS(Least Mean Square)adaptive filtering algorithm.The step size of
this algorithm increases automaticly at the beginning of this algorithm or when unknown system is chan-
ging with time,and it would be smaller during the steady state.This algorithm avoid the shortage of chan-
ging step size of SVSLMS,variable step size LMS based on Sigmoid function,in the process of the adaptive
steady state.The performance of this paper algorithm is better than that of SVSLMS with the theoretical
analysis and computer simulations.
Platform: |
Size: 69632 |
Author:Johnson |
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Description: Adaptive Filtering Primer with MATLAB® clearly explains the fundamentals of adaptive filtering supported by numerous examples and computer simulations. The authors introduce discrete-time signal processing, random variables and stochastic processes, the Wiener filter, properties of the error surface, the steepest descent method, and the least mean square (LMS) algorithm. They also supply many MATLAB® functions and m-files along with computer experiments to illustrate how to apply the concepts to real-world problems. The book includes problems along with hints, suggestions, and solutions for solving them. An appendix on matrix computations completes the self-contained coverage.-Adaptive Filtering Primer with MATLAB® clearly explains the fundamentals of adaptive filtering supported by numerous examples and computer simulations. The authors introduce discrete-time signal processing, random variables and stochastic processes, the Wiener filter, properties of the error surface, the steepest descent method, and the least mean square (LMS) algorithm. They also supply many MATLAB® functions and m-files along with computer experiments to illustrate how to apply the concepts to real-world problems. The book includes problems along with hints, suggestions, and solutions for solving them. An appendix on matrix computations completes the self-contained coverage. Platform: |
Size: 2319360 |
Author:hungfu |
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Description: 一个用来学习和使用的标准kalman和lms变步长最小均方滤波算法源程序代码-One source code to learn and use the standard kalman and least mean square filtering algorithm Platform: |
Size: 2048 |
Author:王伟 |
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Description: This paper investigates the Wiener and least
mean square (LMS) algorithms in the design of traversal tap
delay line filters for the purpose of compensating the effect of
the communication channel. The designed equalizers remove
the distortion caused by the channel from the transmitted
signal without requiring any specific model or state-space
information. The first approach is based on the a recursive
Wiener filtering procedure and is designed using the Wiener-
Hopf equation. On the other hand, the second approach uses
the LMS algorithm and investigates the effect of different
step sizes on the speed of the conversion and the accuracy of
the overall algorithm. Simulation results are presented and
both schemes are compared under different distortion levels
and signal to noise ratio(SNR) values via impulse response,
frequency response and ABER simulations. Platform: |
Size: 10759168 |
Author:nagarjun |
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Search - least mean square filtering