Identification of an ARX model with impulse noise using a variable step size information gradient algorithm based on the kurtosis and minimum Renyi error entropy

2021 ◽  
Author(s):  
Shaoxue Jing
Keyword(s):  
Impulse Noise ◽  
Gradient Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Arx Model ◽  
Size Information ◽  
Variable Step

A new adaptive variable step size natural gradient BSS algorithm

2021 ◽  
pp. 1-12
Author(s):  
Junqing Ji ◽  
Xiaojia Kong ◽  
Yajing Zhang ◽  
Tongle Xu ◽  
Jing Zhang
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Coefficient ◽  
Gradient Algorithm ◽  
Variable Step Size ◽  
Natural Gradient ◽  
Threshold Method ◽  
Step Size ◽  
Morphological Component Analysis ◽  
Variable Step ◽  
Separation Degree

The traditional blind source separation (BSS) algorithm is mainly used to deal with signal separation under the noiseless model, but it does not apply to data with the low signal to noise ratio (SNR). To solve the problem, an adaptive variable step size natural gradient BSS algorithm based on an improved wavelet threshold is proposed in this paper. Firstly, an improved wavelet threshold method is used to reduce the noise of the signal. Secondly, the wavelet coefficient layer with obvious periodicity is denoised using a morphological component analysis (MCA) algorithm, and the processed wavelet coefficients are recombined to obtain the ideal model. Thirdly, the recombined signal is pre-whitened, and a new separation matrix update formula of natural gradient algorithm is constructed by defining a new separation degree estimation function. Finally, the adaptive variable step size natural gradient blind source algorithm is used to separate the noise reduction signal. The results show that the algorithm can not only adaptively adjust the step size according to different signals, but also improve the convergence speed, stability and separation accuracy.

STOCHASTIC INFORMATION GRADIENT ALGORITHM WITH GENERALIZED GAUSSIAN DISTRIBUTION MODEL

2012 ◽  
Vol 21 (01) ◽  
pp. 1250006 ◽  
Author(s):  
BADONG CHEN ◽  
JOSE C. PRINCIPE ◽  
JINCHUN HU ◽  
YU ZHU
Keyword(s):  
Gradient Algorithm ◽  
Distribution Model ◽  
Variable Step Size ◽  
Step Size ◽  
Dispersion Parameters ◽  
Gaussian Density ◽  
Variable Step ◽  
Generalized Gaussian Density ◽  
Automatic Switching ◽  
Gaussian Distribution Model

This paper presents a parameterized version of the stochastic information gradient (SIG) algorithm, in which the error distribution is modeled by generalized Gaussian density (GGD), with location, shape, and dispersion parameters. Compared with the kernel-based SIG (SIG-Kernel) algorithm, the GGD-based SIG (SIG-GGD) algorithm does not involve kernel width selection. If the error is zero-mean, the SIG-GGD algorithm will become the least mean p-power (LMP) algorithm with adaptive order and variable step-size. Due to its well matched density estimation and automatic switching capability, the proposed algorithm is favorably in line with existing algorithms.

scholarly journals Modified Variable Step Size FLOM-CMA Based on Lorentzian Function under Impulse Noise

2018 ◽  
Vol 232 ◽  
pp. 04061
Author(s):  
Xichang Liao ◽  
Yingke Lei
Keyword(s):  
Cost Function ◽  
Impulse Noise ◽  
Variable Step Size ◽  
Step Size ◽  
Simulation Experiments ◽  
Phase Rotation ◽  
Lorentzian Function ◽  
Equalization Algorithm ◽  
Variable Step ◽  
The Cost

Aiming at the problem that the traditional equalization algorithm under impulse noise is difficult to suppress impulse noise and achieve equalization, a new modified variable step size FLOM-CMA algorithm based on Lorentzian function is proposed in this paper. The proposed algorithm has modified the cost function to made full use of the phase information of signal to correct the phase rotation. Also, by adjusting the FLOM of cost function, that impulse noise is restrained effectively. Furthermore, the Lorentzian function is use to update the step size to make sure it is is appropriate for each equalization point. Simulation experiments show that, compared with FLOM-CMA and VS-FLOM-CMA, the proposed algorithm that the proposed algorithm achieves higher convergence accuracy with similar convergence speed. And under different impulse noise conditions, the robustness of the algorithm is better.

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