搜索资源列表
lpc_m
- 实现LPC线性预测分析,能够提取AR模型参数并进行DCT变换处理
Lattice_Verilog
- 本文讨论了AR模型及线性预测的原理，在浮点型DSP TMS320C6713B上实现了语音信号线性预测系数(LPC)的提取，并利用LPC系数用Verilog语言实现了AR模型的Lattice结构。
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
LogOn
- c#实现线性回归预测,线性回归时一个有用的技术，本文展示了一种带权重的自回归模型-Linear regression is a useful technique for representing observed data by a mathematical equation This article presents a C# implementation of a weighted linear regression c sharp AR auto regression predictio
33957049WTE
- 阐述了GPS坐标时间序列分析的原理和方法，包括线性拟合、相关函数法、AR（P）模型预测、功率谱、小波谱以及小波熵，通过仿真实验验证了各种方法的优点。-2. The theory and methods of GPS coordinate time series analysis are expounded, including linear fitting, correlation function, AR (P) model prediction, power spectrum, wavele
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,
Burg.m
- AR模型的Burg算法，线性预测。 场景为V2V车载场景，使用Jakes模型-Burg s algorithm based on AR model,whose environment is V2V.
LMS
- LMS算法实现一阶AR模型的线性预测估计(LMS algorithm for linear prediction estimation of first order AR model)
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