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LMS_RLS_sim
功能描述:测试LMS与RLS算法,比较两种算法的收敛特性
文件名:LMS_RLS_sim.m
测试用例:
x(n)+a1*x(n-1)+a2*x(n-2)=e(n),a1=-1.6,a2=0.81,e(n)为高斯白噪声
文件输出:系数a1的值
调用函数:function [A] = LMS_Algo(M,N,mu,xn)
被调用:无
作者:mingcheng
编写时间:2009-10-13
修改时间:2009-10-13
版本:V1.0 ( Function Description: Test LMS and RLS algorithm, the convergence characteristics were compared file name: LMS_RLS_sim.m test case: x (n)+ a1* x (n-1)+ a2* x (n-2) = e (n), a1 =- 1.6, a2 = 0.81, e (n) is Gaussian white noise file output: the value of coefficient a1 call the function: function [A] = LMS_Algo (M, N, mu, xn) is called: No of: mingcheng write time :2009-10-13 modified :2009-10-13 version: V1.0 )
- 2010-07-11 12:15:54下载
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PROFANA
1. It is necessary you have defined on Matlab the X - data matrix. Size of matrix must
be n-by-(1+p) sample=column 1, variables=column 2:p). And alpha - significance
(default = 0.05).
2. For running this file it is necessary to call the rafisher function as
PROFANA(X,alpha). Please see the help PROFANA.
3. Once you input the arguments, it will appears your results. (
1. It is necessary you have defined on Matlab the X- data matrix. Size of matrix must
be n-by-(1+p) sample=column 1, variables=column 2:p). And alpha- significance
(default = 0.05).
2. For running this file it is necessary to call the rafisher function as
PROFANA(X,alpha). Please see the help PROFANA.
3. Once you input the arguments, it will appears your results. )
- 2009-12-23 14:49:09下载
- 积分:1