Description: 基于Cholesky分解的混沌时间序列Volterra预测-based on the Cholesky decomposition Volterra chaotic time series prediction Platform: |
Size: 84992 |
Author:四度 |
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Description: WallFlower算法实现中涉及到的两个基础算法的实现，分别是线性预测，和k均值算法，很方便的调用。-Algorithm WallFlower involved in both basis algorithm, namely linear prediction, and the k-means algorithm, it is convenient to call. Platform: |
Size: 52224 |
Author:欧阳博 |
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Description: 分别采用维纳滤波和l-d算法设计一个6阶前向线性预测器，给出设计过程，matlab程序。
要求：1、得到预测器的权向量和预测误差功率
2、画出预测阶数和预测误差功率的曲线
3、在使用l-d算法时，假设 ， ，…… 未知
-Wiener filter and were used to design a 6-ld algorithm prior to the linear predictor order, given the design process, matlab program. Requirements: 1, are predictors of weight vectors and the prediction error power of 2, draw the prediction order and the prediction error power, Curve 3, in the use ld algorithms, assumptions, ... ... unknown Platform: |
Size: 1024 |
Author:xiaosa |
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Description: 用一个2阶的AR模型产生一平稳的随机过程s(n)。
其中， ， ， 可任意选定。
再产生一方差为 的白噪声 。接收信号为
，
要求对接收信号进行维纳滤波，输出信号为 。
1， 给定维纳滤波器的长度M，考察代价和函数与SNR之间的关系。
2， 给定SNR,考察代价函数与信噪比之间的关系。
3， 做一步预测，代价函数随滤波器的长度和信噪比的变化关系。
-With a 2-order AR model to generate a stationary random process s (n). Among them,,, can be arbitrarily selected. Accrue to the white noise variance. Received signal, the received signal requires filtering, the output signal. 1, given the length of the Wiener filter M, investigation costs and the relationship between function and SNR. 2, for a given SNR, the cost function and examine the relationship between signal to noise ratio. 3, do step prediction, the cost function with the filter length and variation of signal to noise ratio. Platform: |
Size: 128000 |
Author:丁倩 |
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Description: The digital baseband predistorter is an effective technique to compensate for the nonlinearity of
power amplifiers (PAs) with memory effects. However, most available adaptive predistorters based on direct
learning architectures suffer from slow convergence speeds. In this paper, the recursive prediction error
method is used to construct an adaptive Hammerstein predistorter based on the direct learning architecture,
which is used to linearize the Wiener PA model. The effectiveness of the scheme is demonstrated on a digital
video broadcasting-terrestrial system. Simulation results show that the predistorter outperforms previous
predistorters based on direct learning architectures in terms of convergence speed and linearization. A similar
algorithm can be applied to estimate the Wiener PA model, which will achieve high model accuracy. Platform: |
Size: 238592 |
Author:sali |
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Description: Solves prediction problem with statistical filters with some standard algorithms (Wiener, LS, etc.) Platform: |
Size: 3072 |
Author:Danaroth |
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Description: 利用维纳一步纯预测方法实现对信号生成模型的参数估计。(Parameter estimation of signal generation model is realized by using Wiener simple prediction method.) Platform: |
Size: 1024 |
Author:小莫洛
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Description: MATLAB codes for adaptive filtering using least mean square, nominal LMS and Wiener filter using forward linear prediction and backward linear prediction. Platform: |
Size: 176128 |
Author:MHQ
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