搜索资源列表
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
SOM_PREDICTION
- SOM-BASED TIME SERIES PREDICTION Training Time Series Testing Time Series Local Linear Mapping (LLM) Vector-Quantized Temporal Associative Memory (VQTAM) Global RBF (GRBF, download here) and Local RBF (KRBF) Local AR predictors using data
DGM
- 离散灰色预测模型和AR预测模型的组合预测，matlab-Discrete gray prediction model and predict the combination of AR prediction model, matlab
BVAR_Gibbs
- 贝叶斯分析，比较复杂的自回归分析。VAR模型，注意比AR要先进的多！-Bayesian estimation, prediction and impulse response analysis in VAR models using the Gibbs sampler.
ar-kalman-1
- 基于卡尔曼算法的AR模型系数预测，利用卡尔曼滤波算法对AR模型的系数进行实时更新，可以观察到预测准确度有明显提高-Kalman algorithm based on AR model coefficients predicted using the Kalman filter AR model coefficients for real-time updates can be observed significantly improve prediction accuracy
ar(5)
- 一个五阶的自回归短期预测模型的MATLAB程序-A AUTOREGRESSion model used to prediction
rls
- 自己编写的AR过程信号线形预测器RLS算法。-I have written of the AR process of linear prediction RLS algorithm
a
- time series prediction codes. for arma and ar modelling
b
- time series prediction codes. for arma and ar modelling
ar2
- a matlab code for AR prediction
Timeprediction-AR-MATLAB
- 时间序列AR模型预测的matlab源文件，可直接运行，有仿真结果。-Time series prediction of the AR model matlab source file, can be directly operation, the simulation results.
forecast
- AR模型预测算法，使用BIC准则定阶，简单易懂-AR model prediction, using the BIC criteria set order
AR-model
- 基于神经网络的一步预测程序，用于混响序列的目标回波检测-Step prediction procedure based on neural networks for reverb-echo sequence detection
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,
computerwork_2
- 2. 设 是窄带信号，定义 是在 区间上均匀分布的随机相位。 是寬带信号，它是一个零均值、方差为1的白噪音信号e(n)激励一个线性滤波器而产生，其差分方程为 。 1） 计算 和 各自的自相关函数，并画出其函数图形。根据此选择合适的延时，以实现谱线增强。 2） 产生一个 序列。选择合适的 值。让 通过谱线增强器。画出输出信号 和误差信号e(n)的波形，并分别与 和 比较。 -Computer Experiments: 1. Consider an AR process x
prediction-methods-for-hydrology
- 径流预报常用的几种模型：AR模型，BP模型，RBF模型，GM(1,N)模型；预报数据预处理方法：自相关函数以及偏自相关函数确定法；EMD方法-Several commonly used runoff forecasting model: AR model, BP model, RBF model, GM (1, N) model forecast data preprocessing methods: autocorrelation function and partial autocorre
AR
- Autoregressive model for forecasting and prediction
AR
- 基于现行自回归预测模型的MATLAB代码，通过历史数据预测当前数据并实时修正当前权重参数值(Based on the MATLAB code of the current autoregressive prediction model, the current data is predicted by historical data and the current weight parameter values are corrected in real time)
AR
- AR模型，用于AR预测很准的，使用两种方法，最大熵法以及最小二乘法。(The AR model for accurate AR prediction)