Description: 递归式最小均方（RLS）算法的基本思想是力图使在每个时刻对所有已输入信号而言重估的平方误差的加权和最小，这使得RLS算法对非平稳信号的适应性要好。与LMS算法相比，RLS算法采用时间平均，因此，所得出的最优滤波器依赖于用于计算平均值的样本数，而LMS（NLMS）算法是基于集平均而设计的，因此稳定环境下LMS（NLMS）算法在不同计算条件下的结果是一致的-Recursive least-mean-square (RLS) algorithm for the basic idea is to try to make in every moment of all the input signal in terms of re-evaluation of the weighted squared error and the smallest, which allows non-stationary RLS algorithm for adaptive signal better. Compared with the LMS algorithm, RLS algorithm uses the average time, therefore, the resulting optimal filter depends on the used to calculate the average number of samples, and the LMS (NLMS) algorithm is designed based on set average, and therefore a stable environment LMS (NLMS) algorithm in different conditions, the results of the calculation is consistent Platform: |
Size: 3072 |
Author:闫丰 |
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Description: VHDL code for linear prediction filter based on RLS (recursive least square).
Filter order is set to 4, bit precision set to 12 bits for input and output. Signals are complex signals. Platform: |
Size: 5120 |
Author:徐滨 |
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Description: 自适应滤波器的核心是自适应算法。最小均方算法(LMS) 和递归最小二次方算法RLS) 。通过对这两种算法的推导过程,可以清楚的了解到自适应算法的理论体系,-Adaptive filter is the core of adaptive algorithm. Least mean square algorithm (LMS) and recursive least quadratic algorithm RLS). Through the derivation of these two algorithms, adaptive algorithms can clearly understand the theoretical system,
Platform: |
Size: 3072 |
Author:王柯澜 |
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Description: 产生一个随机信号和两个不同频率但频率间隔很小的正弦信号，要求对两信号之和进行如下分析：
（1） 求该随机信号的自相关性系数、自相关函数，画出对应的图形；
（2） 利用不同的参数建模方法求出两个随机信号的功率谱；
（3） 利用极大似然估计、递推最小二乘法等常用的参数估计方法估计所建模型，包括AR模型、MA模型和ARMA模型的的参数，阶次自定；并与Matlab工具箱里的一些建模函数的运算结果进行比较；
（4） 利用陷波滤波和MUSIC滤波方法对该信号的频谱进行估计；
（5） 利用Wiener滤波、LMS滤波对该含噪声的正弦信号进行去噪声处理；
（6） 假设该信号是一个飞行器的某个方向的线位移信号，可否利用Kalman滤波对该信号进行滤波？
（7） 利用高阶谱理论对该信号进行谱估计和相应的AR模型、MA模型和ARMA模型估计；
（8） 利用小波变换（变换函数可以直接用Matlab里的函数）对该信号进行去噪声处理，并和前面的去噪声方法进行比较。
-Produce a random signal and two different frequency but frequency interval small sine signals, required to make the following analysis of two signals:
(1) for the random signal from the correlation coefficients, the autocorrelation function and draw the corresponding graphics
(2) make use of different parameters modeling method for out two random signal power spectrum
(3) using maximum likelihood estimation, recursive least square method and common parameters estimation method estimates that the model, including AR model, MA model and the parameters of the ARMA model we, order time decided oneself With Matlab toolbox and some of the modeling function is used compared the
(4) use trapped wave filter and MUSIC of the signal filter method of spectrum to estimate
(5) use Wiener filtering, LMS filtering on the sinusoidal signal with noise to noise treatment
(6) assumes the signal is a vehicle of a certain direction of displacement signal line, can you use Kalman filtering on the Platform: |
Size: 6144 |
Author:李思青 |
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Description: 摘要:分析了最小均方误差滤波和递归最小二乘滤波算法、自适应滤波的神经网络方法、
基于QR分解的方法、统一模型下的自适应滤波及基于高阶累积量的自适应算法的优缺点，并对自适应滤波算法的未来发展做了展望。
-the author analyzes the minimum mean square error filter and recursive least square filter algorithm, an adaptive filter neural network method,
Based on the decomposition of the QR unified model method, an adaptive filter and based on the cumulant adaptive algorithm of their advantages and disadvantages, and on the
The algorithm is applied to the development of the future is prospected.
Platform: |
Size: 877568 |
Author:张会先 |
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Description: The Recursive least squares (RLS) is an adaptive filter which recursively finds the coefficients that minimize a weighted linear least squares cost function relating to the input signals. This is in contrast to other algorithms such as the least mean squares (LMS) that aim to reduce the mean square error. In the derivation of the RLS, the input signals are considered deterministic, while for the LMS and similar algorithm they are considered stochastic. Compared to most of its competitors, the RLS exhibits extremely fast convergence. However, this benefit comes at the cost of high computational complexity. Platform: |
Size: 9216 |
Author:rajendra |
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Description: It has adaptive fiter algorithms such as Least mean Square algorithm and Recursive least mean square algorithm matlab codes. Platform: |
Size: 6144 |
Author:hari |
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Description: 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 contrast to other algorithms such as the least mean squares (LMS) that aim to reduce the mean square error. In the derivation of the RLS, the input signals are considered deterministic, while for the LMS and similar algorithm they are considered stochastic. Compared to most of its competitors, the RLS exhibits extremely fast convergence. However, this benefit comes at the cost of high computational complexity.) Platform: |
Size: 1024 |
Author:清微
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Description: 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 signal processor, the principle of adaptive filter and the main application fields, the basic theory of adaptive filter is introduced, and the basic principle, algorithm and design method of adaptive filter are described in detail. In this paper, two basic adaptive algorithms, namely the minimum mean square error (LMS) algorithm and recursive least squares (RLS) algorithm are introduced and analyzed in detail, and the advantages and disadvantages of the two algorithms are compared in detail. Finally, the adaptive filter is implemented by DSP. The experimental results show that the adaptive filter has better filtering effect.) Platform: |
Size: 1024 |
Author:古古怪怪asd
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Description: 递归最小二乘(RLS)是一种自适应滤波算法，它可以递归地找到最小化加权线性最小二乘代价函数与输入信号相关的系数。这种方法与其他算法相比较，例如最小均方(LMS)，旨在减少均方误差。在RLS的推导中，输入信号被认为是确定性的，而对于LMS和类似的算法，它们被认为是随机的。(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 contrast to other algorithms such as the least mean squares (LMS) that aim to reduce the mean square error. In the derivation of the RLS, the input signals are considered deterministic, while for the LMS and similar algorithm they are considered stochastic.) Platform: |
Size: 1024 |
Author:大东东 |
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Search - recursive least square filter