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: matlab编程，数字信号实验维纳滤波，估计AR模型参数，具有良好的滤波效果。
-Matlab programming, digital signal experimental Wiener filter, it is estimated that the AR model parameters, has good filtering effect. Platform: |
Size: 1024 |
Author:胡迪 |
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Description: Hammerstein_Wiener模型最小二乘向量机辨识及其应用 EI文章-Hammerstein_Wiener model identification and application of least squares vector machines EI article Platform: |
Size: 480256 |
Author:YAN YU |
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Description: 功率放大器的Wiener Hammerstein建模及记忆多项式模型-Modeling PA with Wiener Hammerstein and Memory Polynomial Method Platform: |
Size: 3072 |
Author:张帆 |
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Description: Power amplifier is essential component in
communication systems and it’s inherently nonlinear. This essay
mainly investigate a based on Hammerstein predistorter , a
memory polynomial predistorter. The Hammerstein predistorter
is designed specifically for power amplifiers that can be modeled
as a Wiener system. The memory polynomial predistorter can
correct both the nonlinear distortions and the linear frequency
response that may exist in the power amplifier. Platform: |
Size: 167936 |
Author:sali |
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Description: This paper presents a comparative study on the
suitability of using Hammerstein or Wiener models to
identify the power amplifier (PA) nonlinear behavior
considering memory effects. This comparative takes into
account the operational complexity regarding the
identification process as well as their accuracy to follow the
PA behavior. Both identified PA models will be used to
estimate a Hammerstein based predistorter in order to see
which model combination provides better linearization
results. In addition, two adaptive algorithms for
predistorting both PA models are compared in terms of
accuracy and converge speed. Platform: |
Size: 276480 |
Author:sali |
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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: Model-based control strategies like model predictive
control (MPC) require models of process dynamics accurate
enough that the resulting controllers perform adequately in
practice. Often, these models are obtained by fitting convenient
model structures (e.g., linear finite impulse response (FIR) models,
linear pole-zero models, nonlinear Hammerstein or Wiener
models, etc.) to observed input–output data. Real measurement
data records frequently contain “outliers” or “anomalous data
points,” which can badly degrade the results of an otherwise
reasonable empirical model identification procedure. This paper
considers some real datasets containing outliers, examines the
influence of outliers on linear and nonlinear system identification,
and discusses the problems of outlier detection and data cleaning.
Although no single strategy is universally applicable, the Hampel
filter described here is often extremely effective in practice. Platform: |
Size: 119808 |
Author:JTNT |
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Search - wiener-hammerstein