Description: Second and Higher-Order Statistics based Multiple-Input-Multiple-Output System
Blind Identification Matlab Code
-Second and Higher-Order Statistics based Multiple-Input-Multiple-Output System Blin Matlab d Identification Code Platform: |
Size: 22528 |
Author:于蕾 |
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Description: 基于lms的系统辨识的matlab仿真有点小问题-Based on the LMS system identification of the matlab simulation a bit small problem Platform: |
Size: 1024 |
Author:哗哗 |
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Description: 简单的最小二乘逼近算法，用于系统辨识，方便修改噪声参数和系统参数，为系统辨识和仿真作业的源代码。-Simple least-squares approximation algorithm for system identification to facilitate the modification of system parameters and noise parameters for the system identification and simulation of the source code of operation. Platform: |
Size: 1024 |
Author:lsw_hit |
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Description: System identification with adaptive filter using full and partial-update Least-Mean-Squares -System identification with adaptive filter using full and partial-update Least-Mean-Squares Platform: |
Size: 4096 |
Author:Peter Tiong |
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Description: 包括：三种LMS算法实现AR(2)的预测，法2、3用递推计算Km,两者区别在于d(n)的取法略微不同；用LSL和FTF算法实现简单的系统辨识。-Include: three LMS algorithm AR (2) the forecast, France 2,3 calculated with recursion Km, whichever is the difference between d (n) of a slightly different取法 LSL and the FTF algorithm with simple system identification. Platform: |
Size: 975872 |
Author: |
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Description: LMS Algorithm Demo - A system identification by the usage of the LMS algorithm.-LMS Algorithm Demo- A system identification by the usage of the LMS algorithm. Platform: |
Size: 1024 |
Author:tom |
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Description: FIR_A=[1 1 2]
FIR_B=[2 1 1]
function [w_out mse_out ref_out] = LMS(FIR_A,FIR_B,1,wave=square)
[w mse ref res iter] = LMS(FIR_A,FIR_B,L,wave)
LMS filter to solve the system identification problem represented below:
---------- ----------
------- FILTER A -----out_A----- FILTER X ---out--
| ---------- ---------- |
in | |
----| |+
| ---------- - -----
------- FILTER B -----out_B-------------------- SUM ---error---
---------- -----
FILTER_X is unknown and to be derived. This problem is called "filter matching"
and is encountered when one needs to augment a certain filter (A)
in order to match the behavior of a reference filter (B).-FIR_A=[1 1 2]
FIR_B=[2 1 1]
function [w_out mse_out ref_out] = LMS(FIR_A,FIR_B,1,wave=square)
[w mse ref res iter] = LMS(FIR_A,FIR_B,L,wave)
LMS filter to solve the system identification problem represented below:
---------- ----------
------- FILTER A -----out_A----- FILTER X ---out--
| ---------- ---------- |
in | |
----| |+
| ---------- - -----
------- FILTER B -----out_B-------------------- SUM ---error---
---------- -----
FILTER_X is unknown and to be derived. This problem is called "filter matching"
and is encountered when one needs to augment a certain filter (A)
in order to match the behavior of a reference filter (B). Platform: |
Size: 2048 |
Author:dasu |
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Description: 最小均方算法是一种自适应滤波算法，这里的Matlab程序用于根据LMS最新均方识别一个线性噪声系统-LMS algorithm is an adaptive filter algorithm, where the Matlab program for the latest according to the mean square LMS noise system identification of a linear Platform: |
Size: 1024 |
Author:lluu |
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Description: 利用lms算法和rls算法，对通过给定系统h的随机信号进行自适应滤波，通过抽头w对系统进行逆辨识与辨识，同时产生MSE 即均方误差，来描述对信号恢复的效果。-Using lms algorithm and rls algorithms h through a given system adaptive filtering of random signals, the system through the tap w reverse identification and recognition, while the mean square error MSE generated to describe the effect on signal recovery. Platform: |
Size: 1024 |
Author: |
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Description: 自适应信号处理中的格型LMS算法，并与横向LMS做了比较。应用环境是系统辨识-lattice LMS algorithm of adaptive signal processing were compared with the horizontal LMS algorithm. Application environment is the system identification Platform: |
Size: 1024 |
Author:韩科 |
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Description: These are codes for system identification using various algorithm like LMS,RLS,PSO+FLA-These are codes for system identification using various algorithm like LMS,RLS,PSO+FLANN Platform: |
Size: 2048 |
Author:rakesh |
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Description: 用Matlab实现自适应信号处理中的系统辨识，自适应处理器采用自适应线性组合器，未知被控系统采用AR model。用了LMS算法和最速下降法实现。-Realise system identification in adaptive signal processing with matlab.The LMS algorithm and Speedest Descent method are used. Platform: |
Size: 513024 |
Author:mingzhan |
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Description: lms自适应算法源码，同时完成系统辨识仿真(LMS adaptive algorithm source code, at the same time complete system identification simulation) Platform: |
Size: 1024 |
Author:密
|
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