多部预测自校正控制的算法仿真程序，用matlab写的。用的RLS带遗忘的最小二乘法递推。-Forecast for more than self-tuning control algorithm simulation program, written using matlab. Used in the RLS with forgetting recursive least square method.

This paper compares performance of nite impulse response (FIR) adaptive linear equalizers based on the recursive least-squares (RLS) and least mean square(LMS) algorithms in nonstationary uncorrelated scattering wireless channels. Simulation resul

递归最小二乘算法，来至MIT大学的wingate教授，含3个源码-rls.m- Basic recursive least squares. qr_rls.m- Square-root version of RLS using a QR decomposition. bqr_rls.m- Block square-root version of RLS using a QR decomposition (you can add multiple rows at a time).

LMS算法和NLMS算法以及RLS算法演示程序-Lest Mean Square (LMS) Algorithm Recursive Least Square (RLS) Algorithm Normalized LMS (NLMS) Algorithm Demo