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Description: 基于Hammerstein模型对传感器进行建模，可以将其分解为非线性和线性环节，设计完非线性补偿器之后，带入到实验数据，验证补偿效果
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Size: 1764 |
Author: 许思淼 |
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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
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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.
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Size: 1024 |
Author: 胡迪 |
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Description: 将动态标定实验的阶跃响应数据进行处理之后，分离实验装置中的非传感器造成的影响，而后利用PSO算法进行传感器的建模-Dynamic calibration of the experimental step response data of the deal, the separation of experimental apparatus in the impact of non-sensor, and then use PSO algorithm sensor modeling
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Size: 2048 |
Author: 许思淼 |
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Description: 基于Hammerstein模型对传感器进行建模，可以将其分解为非线性和线性环节，设计完非线性补偿器之后，带入到实验数据，验证补偿效果-Hammerstein model based on sensor modeling, can be decomposed into non-linear and linear aspects of the design after the End of the nonlinear compensator, into the experimental data to verify the compensation effect
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Size: 1024 |
Author: 许思淼 |
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Description: 数字预失真系统，功放是Wiener-Hammerstein模型，预失真是记忆多项式模型，自适应算法采用RLS-LMS混合算法不。-Digital pre-distortion system, power amplifier is a Wiener-Hammerstein model, pre-distortion is memory polynomial model, using RLS-LMS adaptive algorithm hybrid algorithm does not.
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Size: 1024 |
Author: baggio |
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Description: 本文提出了一种新的基于细菌生存优化(Bacterial Foraging Optimization –BFO)的非线性模型辨识方法。它是利用群集智能仿生BFO算法对一类Hammerstein系统进行辨识，从而估计出它的参数模型-This paper presents a new optimization based on bacterial survival (Bacterial Foraging Optimization-BFO) nonlinear model identification methods. It is the use of bionic swarm intelligence algorithm for a class of BFO Hammerstein system identification, to estimate the parameters of its model
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Size: 75776 |
Author: 黄伟锋 |
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Description: 借鉴Hammerstein模型，对传感器进行建模，并仿真-Reference Hammerstein model, the sensor model and simulation
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Size: 2048 |
Author: 静静 |
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Description: matlab仿真dpd，预失真的算法，在读博士仿真源码，包括volterra，saleh，多项式。-matlab simulation dpd, pre-distortion algorithm, Ph.D. simulation source code, including the volterra, saleh, polynomials.
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Size: 320512 |
Author: zhudewei |
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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
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Size: 480256 |
Author: YAN YU |
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Description: miso hammerstein models
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Size: 2647040 |
Author: fantasy |
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Description: Some articles on MISO and Hammerstein models
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Size: 1757184 |
Author: fantasy |
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Description: In a number of vibration applications, systems under study are slightly non-linear. Cascade of Hammerstein models conveniently allows one to describe such systems.
The Hammerstein Toolbox provides a simple method based on a phase property of exponential sine sweeps
to estimate the structural elements (Kernels) of such a model from only one measured response of the system.
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Size: 10240 |
Author: nivalis |
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Description: 利用两步法辨识Hammerstein模型，用到了特殊的输入信号，已经上传。-ication of Hammerstein Model using two-step method, to use a special input signal has been uploaded.
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Size: 2048 |
Author: lizhong |
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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
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Size: 3072 |
Author: 张帆 |
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Description: 基于RBF神经网络的Hammerstein模型辨识,研究建立被控对象或过程数学模型的一种理论和方法-The Hammerstein model based on RBF neural network identification, research to establish the mathematical model of controlled object or process a theory and method
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Size: 119808 |
Author: 飞华 |
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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.
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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.
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Size: 238592 |
Author: sali |
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Description: 这是Hammerstein系统加输入死区的递推增广最小二乘法参数辨识程序，可以运行。-This is a plus input recursion system Hammerstein dead Augmented least squares method parameter identification program can be run.
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Size: 2048 |
Author: 李磊伟 |
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Description: 带有死区的hammerstein模型辨识(Identification of Hammerstein model with dead time)
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Size: 149504 |
Author: jairy
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