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变步长LMS算法!用于系统识别的,用MATLAB编写-modified LMS algorithm! For system identification, prepared using MATLAB
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matlab实现系统辨识 采用LMS（最小均方误差）估计 随机信号均值为0方差为1，加入高斯白噪声-matlab for system identification using LMS (least mean square error) estimation of random signal with mean 0 variance 1, by adding Gaussian white noise
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应用LMS算法，用Matlab编程实现系统识别.-Application of LMS algorithm, using Matlab programming system identification.
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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
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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,
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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
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对于一阶系统辨识，分别采用LMS、RLS、仿射投影算法来估计参数-For a first-order system identification, respectively, using LMS, RLS, affine projection algorithm to estimate the parameters
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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
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