文件名称:LMS_RLS_sim
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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 Descr iption: 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
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 Descr iption: 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
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LMS_RLS_sim.m
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