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ARandARMA
- 实现了数据从文件的输入，ar模型预测，arma模型预测，卡尔曼滤波器模型预测，利用图形用户界面编写-Realized the data from the file input, ar model predictions, arma model prediction, Kalman filter model predictions, using a graphical user interface for the preparation of
AR
- matlab写的用于时间序列预测的AR模型的程序，对于线性的时间序列效果还是可以的。-Matlab written procedures for time series forecasting AR model, the linear effect of time-series.
RLS
- 本程序基于一阶AR模型，u(n)=-0.99u(n-1)+v(n)的线性预测。白噪声v(n)方差0.995.FIR滤波器的抽头数为2.遗忘因子0.98.用RLS算法实现u(n)的线性预测。并附有仿真图片-This procedure is based on a first-order AR model, u (n) =-0.99u (n-1)+v (n) of the linear prediction. White noise v (n) the number of taps of the t
LMS
- 基于一阶AR模型u(n)=0.99u(n-1)+v(n),白噪声方差0.93627.步长0.05.分别使用M=2和M=3抽头的滤波器，用LMS算法实现u(n)的线性预测估计。并附仿真图已被参考。-Based on a first-order AR model u (n) = 0.99u (n-1) the+v (n), the white noise variance 0.93627 step 0.05. Respectively with M = 2 and M = 3-tap filter,
LMS与RLS对比
- 预测信号由二阶AR模型产生，为二阶线性预测滤波器，LMS算法与RLS算法性能对比(The predicted signal is generated by the two order AR model, and is the two order linear prediction filter,performance comparison between LMS algorithm and RLS algorithm)
RLS算法
- RLS算法对一阶AR模型线性预测，包含完整源码，可以得到最优权值