Nonlinear Noise Canceller by Neural Network with Variable Step Size

2013 ◽  
Vol 433-435 ◽  
pp. 709-712
Author(s):  
Shou Zhong Zhang
Keyword(s):  
Neural Network ◽  
Variable Step Size ◽  
Signal To Noise ◽  
Step Size ◽  
Nonlinear Correlation ◽  
Transversal Filter ◽  
Interference Noise ◽  
Variable Step ◽  
Nonlinear Noise ◽  
Simulation Results

Neural network is acted as noise canceller to implement noise cancel under the condition of interference noise has nonlinear correlation to reference noise. If interference noise has nonlinear correlation to reference noise, the transversal filter has weak effect to cancel the noise in the signal. Neural network has nonlinear characteristic transfer and can solve this problem, and a new variable step size algorithm is proposed to further improve the performance. Computer simulation results show that neural network noise canceller has better signal to noise gain for nonlinear noise.

A Variable Step Size LMS Adaptive Filtering Algorithm and Simulations Based on Computational Verb Theory

2013 ◽  
Vol 756-759 ◽  
pp. 3972-3976 ◽  
Author(s):  
Li Hui Sun ◽  
Bao Yu Zheng
Keyword(s):  
Adaptive Filtering ◽  
Adaptive Algorithm ◽  
Linear Functional ◽  
Functional Relationship ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Filtering Algorithm ◽  
Variable Step ◽  
Simulation Results

Based on traditional LMS algorithm, variable step LMS algorithm and the analysis for improved algorithm, a new variable step adaptive algorithm based on computational verb theory is put forward. A kind of sectorial linear functional relationship is established between step parameters and the error. The simulation results show that the algorithm has the advantage of slow change which is closely to zero. And overcome the defects of some variable step size LMS algorithm in adaptive steady state value is too large.

scholarly journals An Adaptive Fuzzy Neural Network Based on Progressive Gaussian Approximate Filter with Variable Step Size

2021 ◽  
Keyword(s):  
Neural Network ◽  
Full Text ◽  
Fuzzy Neural Network ◽  
Variable Step Size ◽  
Step Size ◽  
Adaptive Fuzzy Neural Network ◽  
Adaptive Fuzzy ◽  
Variable Step ◽  
Fuzzy Neural

Abstract The full text of this preprint has been withdrawn by the authors due to author disagreement with the posting of the preprint. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.

A Denoising Method Based on the Variable Step NLMP for the Pulsar Signal

2012 ◽  
Vol 500 ◽  
pp. 760-765
Author(s):  
Jing Rong Sun ◽  
Gui Ying Zhang
Keyword(s):  
Stable Distribution ◽  
Weight Vector ◽  
Variable Step Size ◽  
Step Size ◽  
Denoising Method ◽  
Performance Simulation ◽  
Variable Step ◽  
Convergence Performance ◽  
Simulation Results ◽  
Better Than

In order to denoise the pulsar signal, a variable step NLMP algorithm was introduced under the-stable distribution. The algorithm introduced a step update factor. By adjusting parameters and error information, the algorithm can adjust the incremental direction of the adaptive filter weight vector accurately, and improve the convergence performance. Simulation results show that the variable step-size NLMP algorithm is better than the NLMP algorithm in the denoising effect in-stable distribution noise environments.

scholarly journals A Variable Step-Size Leaky LMS Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Sihai Zhao ◽  
Jiangye Xu ◽  
Yuyan Zhang
Keyword(s):  
Input Sequence ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Noise Interference ◽  
Parameter Drift ◽  
Fast Convergence Rate ◽  
Variable Step ◽  
Time Average ◽  
Simulation Results

The leaky LMS algorithm has been extensively studied because of its control of parameter drift. This unexpected parameter drift is linked to the inadequacy of excitation in the input sequence. And generally leaky LMS algorithms use fixed step size to force the performance of compromise between the fast convergence rate and small steady-state misalignment. In this paper, variable step-size (VSS) leaky LMS algorithm is proposed. And the variable step-size method combines the time average estimation of the error and the time average estimation of the normalized quantity. Variable step-size method proposed incorporating with leaky LMS algorithm can effectively eliminate noise interference and make the early convergence, and final small misalignments are obtained together. Simulation results demonstrate that the proposed algorithm has better performance than the existing variable step-size algorithms in the unexcited environment. Furthermore, the proposed algorithm is comparable in performance to other variable step-size algorithms under the adequacy of excitation.

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