搜索资源列表
recursionls
- 系统辨识二阶递推最小二乘算法实现。对数据进行了归一化的预处理,并且对原始数据进行一次采样-Second-order recursive least squares system identification algorithm
rls_AR_pred
- 使用基本的递归最小二乘算法来预测实值AR过程信号-use basic RLS algorithm to predict real-valued AR process
RLS1234567890
- Recursive least squares递归最小二乘算法-Recursive least squares
Recursive-least-squares
- 递归最小二乘算法,来至MIT大学的wingate教授,含3个源码-rls.m- Basic recursive least squares. qr_rls.m- Square-root version of RLS using a QR decomposition. bqr_rls.m- Block square-root version of RLS using a QR decomposition (you can add multiple rows at a time).
FIR_RLS
- 实现FIR型的递归最小二乘算法自适应滤波器,能够根据参数选择,程序稳定性好。-Realize the FIR adaptive filter recursive least squares algorithm, according to the parameter selection, a good program stability.
MIMO_OFDM-RLS
- MIMO-OFDM系统中一种改进的递归最小二乘(RLS)信道估计方法可以在不需要任何信道统计信息的前提下,利用前导训练序列和自适应遗忘因子对信道状态参数进行估计。-The modified recursion lest squares (RLS) channel estimation method exploits preamble training sequences and adaptive forgetting factor to estimate channel state par
ELS_almda
- 利用矩阵最小二乘的辨识结果作为递归最小二乘的初值,进行系统辨识-Identification using matrix least squares RLS as a result of the initial value, system identification
Low-Complexity-
- 本文提出了一种低复杂度的变遗忘因子机制用于递归最小二乘恒模约束算法中来抑制干扰。改进的方法通过恒模代价函数的时间平均来调节遗忘因子,从而更快地跟踪干扰并抑制,该文章计算量低,收敛速度快。-This paper presents a low complexity variable forgetting factor recursive mechanism for lscm constraint method to suppress interference. Improved methods be
RLS
- 基于RLS算法的数据预测与MATLAB实现—— 递归最小二乘(RLS)算法是一种典型的数据处理方法,递归最小二乘(RLS)算法在信号自适应滤波分析中广泛应用,递归最小二乘(RLS)算法收敛速度快,且对自相关矩阵特征值的分散性不敏感,然而其计算量较大。-RLS algorithm based on forecast data and MATLAB realization Recursive Least Squares (RLS) algorithm is a typical method
Affine-projection-algorithm
- 在有色输入下,仿射投影算法(APA)由于具有比最小均方(LMS)算法更快的收敛速度和比递归最小二乘(RLS)算法更低的复杂度而受到青睐。-The APA better improves the convergence rate compared with the LMS-type filters, especially in the case of coloured input signals.
LMS_RLS
- lms与rls算法比较,MATLAB 程序仿真,比较两种自适应滤波算法。最小均方(LMS)、递归最小二乘(RLS)(LMS and RLS algorithm comparison, MATLAB program simulation, comparison of two adaptive filtering algorithm. Least mean square (LMS) and recursive least squares (RLS))
LRS
- RLS 递归最小二乘滤波器算法!!!!!!!!!!!!!!!!!!!!!(Recursive least squares (RLS) is an adaptive filter algorithm that recursively finds the coefficients that minimize a weighted linear least squares cost function relating to the input signals. This approach is in
3
- 对基于LMS(最小均方)、NLMS(归一化最小均方)、RLS(递归最小二乘)算法的自适应噪声抵消系统进行MATLAB仿真,发现这几种算法都能从高背景噪声中有效地抑制干扰提取出有用信号,显示出了良好的的收敛性能,相比之下RLS算法去噪效果较好,呈现出更快的收敛速度,更强的稳定性和抑噪能力(the principle of LMS (minimum mean square), NLMS (normalized least mean square), RLS (recursive least squa
FFT
- 本文从高速数字信号处理器的特点、自适应滤波器的原理及主要应用领域入手,介绍了自适应滤波器的基本理论思想,具体阐述了自适应滤波器的基本原理、算法及设计方法。本文中,对两种最基本的自适应算法,即最小均方误差(LMS)算法和递归最小二乘(RLS)算法进行了详细的介绍和分析,并针对两种算法的优缺点进行了详细的比较。最后用DSP实现了自适应滤波器。实验结果表明,该自适应滤波器滤波效果优越。(Starting from the characteristics of high-speed digital si
DOEMOMO3
- 递归最小二乘滤波器算法,包含对于单频正弦信号和实际语音信号的处理结果()
sa_ex8_9
- 递归最小二乘(RLS)算法 智能天线 matlab仿真代码(Matlab simulation of RLS algorithm smart antenna)
SLSL90
- 递归最小二乘滤波器算法,包含对于单频正弦信号和实际语音信号的处理结果()
adaptive filter
- 介绍了噪声抵消的原理和从强噪声背景中自适应滤波提取有用信号的方法,并对最小均 方 (LMS, Least Mean Squares) 、归一化 LMS(NLMS, Normalized Least Mean Squares) 和递推最小二乘 (RLS, Recursive Least Squares) 三种基本自适应算法进行了对比研究。计算机模拟仿真结果表明,这 几种算法都能通过有效抑制各种干扰来提高强噪声背景中的信号检测特性。相比之下, RLS 算法 具有良好的收敛性
FIR_RLS
- RLS滤波算法即递推最小二乘法,其又称为最小二乘法,是最小二乘算法的一类快速算法,递归最小二乘自适应滤波器是对一组已知数据的最佳滤波器,处理过程中没有对输入序列的统计特性做出假定,而是纯决定性的最小化问题。相对于LMS自适应横向滤波器具有更好的性能。(RLS filtering algorithm)
RLS与LMS算法matlab实现
- 经典的LMS与RLS递归最小二乘算法,有中文注释,很好理解(Classic LMS AND RLS recursive least squares algorithm, Chinese notes, very good understanding.)