A Variable-Step LMS Algorithm Based on the Adaptive Noise Cancellation

2013 ◽  
Vol 811 ◽  
pp. 375-379
Author(s):  
Jing Mo ◽  
Wei He ◽  
Ruo Yan Han ◽  
Jing Wei Wu ◽  
Dan Su
Keyword(s):  
Noise Suppression ◽  
Lms Algorithm ◽  
Convergence Time ◽  
Size Factor ◽  
Adaptive Noise Cancellation ◽  
Step Size ◽  
Variable Step ◽  
Unknown System ◽  
Adaptive Noise ◽  
Adaptive Noise Canceller

A variable step long LMS algorithm based on Bessel function was put forward, which established the functional relationship between the step size factor and the error signal. And this algorithm would be applied to the adaptive noise canceller in order to improve the ability of the algorithm of uncorrelated noise suppression. This algorithm has a larger step-size during initial convergence stage or unknown system parameters change in order to get a faster convergence time and tracking speed. Moreover, and it adjusts small step-size to achieve a very small steady-state maladjustment noise after the convergence of the algorithm.

Study on Anti-Noise Materials with Multi-Level Filter Based on the Adaptive Noise Cancellation System

2014 ◽  
Vol 886 ◽  
pp. 390-393
Author(s):  
Jing Mo ◽  
Wei He ◽  
Dan Su ◽  
Jing Wei Wu
Keyword(s):  
Noise Cancellation ◽  
Noise Signal ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Adaptive Noise Cancellation ◽  
Step Size ◽  
Filter Performance ◽  
Multi Level ◽  
Adaptive Noise ◽  
Filtering Effect

It presents the Multi-level filters idea of the adaptive noise cancellation system based on the fact that the adaptive noise cancellation system cant filter out noise signal completely. According to the linear combination and the variable step-size LMS algorithm, it analyzes the effects of the two level filters. Theory analyzing and simulation results prove that the multi-level filter can get a better the filtering effect than the one-filter, which improves the filter performance in terms of the fast convergence speed, tracking speed and the low maladjustment error. And the anti-noise materials with multi-level filter based on the adaptive noise cancellation system has the good de-noising ability of noisy signals.

Research and Design of Adaptive Noise Cancellation Based on Particle Swarm Optimization Algorithm

2012 ◽  
Vol 479-481 ◽  
pp. 1942-1945
Author(s):  
Jie Zhang ◽  
Shi Qi Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Noise Cancellation ◽  
Biological Species ◽  
Lms Algorithm ◽  
Adaptive Noise Cancellation ◽  
Step Size ◽  
Swarm Optimization ◽  
Adaptive Noise

Particle swarm optimization (PSO) is a kind of evolutionary computation technology which simulates the behavior of biological species. The essence of adaptive noise cancellation (ANC) is adjust the weight value of filter based on the input signals, the LMS algorithm is commonly used in this system, However, the convergence behavior and maladjustment of the LMS algorithm is seriously affected by the step-size μ, and the optimum value of μ cannot be determined easily, In this paper, Particle Swarm Optimization with linear decreasing inertia weight is proposed to solve the filter problem instead of LMS, taking the FIR filter of ANC as example, the simulation shows that ANC based on the PSO algorithm is better than classic ANC based on the LMS algorithm, and it gives the satisfactory results.

Multiple-input adaptive seismic noise canceller for the attenuation of nonstationary coherent noise

Geophysics ◽  
2011 ◽  
Vol 76 (6) ◽  
pp. V139-V150 ◽  
Author(s):  
Charlotte Sanchis ◽  
Alfred Hanssen
Keyword(s):  
Seismic Data ◽  
Median Filter ◽  
Variable Step Size ◽  
Step Size ◽  
Coherent Noise ◽  
Time Frequency ◽  
Multiple Input ◽  
Variable Step ◽  
Adaptive Noise ◽  
Adaptive Noise Canceller

Attenuation of coherent noise, typically weather generated noise and more specifically swell noise, is a major concern in seismic. Such noise, usually characterized by its low-frequency content and high amplitudes, is a common problem in seismic acquisition and, in particular, for marine data. We propose a multiple-input adaptive noise canceller as a solution to attenuate nonstationary coherent noise. This filter uses multiple noise sequences to estimate the noise contained in each trace. This noise estimate is then subtracted to obtain an estimate of the trace whose coherent noise has been attenuated. For the implementation of the adaptive filter, we use a variable step-size normalized least mean squares algorithm, as it is known for its simplicity and robustness. In addition, we demonstrate that variable step-size is necessary for this filter to adapt to the changing statistics of the seismic data. This method is tested on real marine seismic data and compared to a time-frequency median filter and a second-order high-pass Butterworth filter. We demonstrate that the multiple-input adaptive noise canceller is a powerful and efficient filter to attenuate swell noise while preserving the seismic reflections. Depending on the noise configuration, it can be used either by itself or in combination with a time-frequency median filter to obtain the best results.

A Novel Variable Step Size LMS Algorithm Based on Modified Hyperbolic Tangent and Its Simulation

2012 ◽  
Vol 490-495 ◽  
pp. 1426-1430 ◽  
Author(s):  
Fu Qing Tian ◽  
Rong Luo
Keyword(s):  
Steady State ◽  
Noise Suppression ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Hyperbolic Tangent ◽  
Step Size ◽  
Low Snr ◽  
Variable Step ◽  
Overall Performance ◽  
Simulation Results

In the paper, a new variable step size LMS algorithm based on modified hyperbolic tangent is presented. In the algorithm, the step size is adjusted by the estimation of the autocorrelation between and .The algorithm introduces the compensation monomial to improve the convergence and the parameters to improve the shape and bottom characteristic of hyperbolic tangent. Therefore, the algorithm has faster convergence, better performance of noise suppression，lower steady state error and misadjustment. The theoretical analysis and simulation results all show that the overall performance of the new algorithm exceeds greatly some existent others under low SNR condition.

Active control for vehicle interior noise using the improved iterative variable step-size and variable tap-length LMS algorithms

2019 ◽  
Vol 67 (6) ◽  
pp. 405-414 ◽  
Author(s):  
Ningning Liu ◽  
Yuedong Sun ◽  
Yansong Wang ◽  
Hui Guo ◽  
Bin Gao ◽  
...  
Keyword(s):  
Steady State ◽  
Noise Control ◽  
Convergence Speed ◽  
Interior Noise ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Vehicle Interior ◽  
Vehicle Interior Noise ◽  
Variable Step

Active noise control (ANC) is used to reduce undesirable noise, particularly at low frequencies. There are many algorithms based on the least mean square (LMS) algorithm, such as the filtered-x LMS (FxLMS) algorithm, which have been widely used for ANC systems. However, the LMS algorithm cannot balance convergence speed and steady-state error due to the fixed step size and tap length. Accordingly, in this article, two improved LMS algorithms, namely, the iterative variable step-size LMS (IVS-LMS) and the variable tap-length LMS (VT-LMS), are proposed for active vehicle interior noise control. The interior noises of a sample vehicle are measured and thereby their frequency characteristics. Results show that the sound energy of noise is concentrated within a low-frequency range below 1000 Hz. The classical LMS, IVS-LMS and VT-LMS algorithms are applied to the measured noise signals. Results further suggest that the IVS-LMS and VT-LMS algorithms can better improve algorithmic performance for convergence speed and steady-state error compared with the classical LMS. The proposed algorithms could potentially be incorporated into other LMS-based algorithms (like the FxLMS) used in ANC systems for improving the ride comfort of a vehicle.

scholarly journals An Active Equalization Method for Lithium-ion Batteries Based on Flyback Transformer and Variable Step Size Generalized Predictive Control

Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 207
Author(s):  
Jianwen Cao ◽  
Bizhong Xia ◽  
Jie Zhou
Keyword(s):  
Energy Consumption ◽  
Lithium Ion Batteries ◽  
Predictive Control ◽  
Lithium Ion ◽  
Generalized Predictive Control ◽  
Variable Step Size ◽  
Size Factor ◽  
Step Size ◽  
Variable Step ◽  
Equalization Method

The inconsistency in large-scale battery pack significantly degrades the performance of electric vehicles. In order to diminish the inconsistency, the study designs an active equalization method comprising of equalizer and equalization strategy for lithium-ion batteries. A bidirectional flyback transformer equalizer (BFTE) is designed and analyzed. The BFTE is controlled by a pulse width modulation (PWM) controller to output designated balancing currents. Under the purpose of shortening equalization time and reducing energy consumption during the equalization process, this paper proposes an equalization strategy based on variable step size generalized predictive control (VSSGPC). The VSSGPC is improved on the generalized predictive control (GPC) by introducing the Step Size Factor. The VSSGPC surmounts the local limitation of GPC by expanding the control and output horizons to the global equalization process without increasing computation owing to the Step Size Factor. The experiment results in static operating condition indicate that the equalization time and energy consumption are reduced by 8.3% and 16.5%, respectively. Further validation in CC-CV and EUDC operating conditions verifies the performance of the equalizer and rationality of the VSSGPC strategy.

A noise resilient variable step-size LMS algorithm

2008 ◽  
Vol 88 (3) ◽  
pp. 733-748 ◽  
Author(s):  
Márcio Holsbach Costa ◽  
José Carlos Moreira Bermudez
Keyword(s):  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Variable Step
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