QX-LMS Adaptive FIR Filters For System Identification

Guan, Xiaochun; Chen, Xiaojing; Wu, Guichu

doi:10.1109/cisp.2009.5301497

Cited by 17 publications

(6 citation statements)

References 4 publications

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“…In the leaky‐LMS algorithm, a leakage factor is introduced, which stabilizes the system and enhances its performance. It has been applied in many applications such as system identification , adaptive noise cancelation , etc.…”

Section: Introductionmentioning

confidence: 99%

Sparse leaky‐LMS algorithm for system identification and its convergence analysis

Salman

2013

Adaptive Control & Signal

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SUMMARYIn this paper, a novel adaptive filter for sparse systems is proposed. The proposed algorithm incorporates a log‐sum penalty into the cost function of the standard leaky least mean square (LMS) algorithm, which results in a shrinkage in the update equation. This shrinkage, in turn, enhances the performance of the adaptive filter, especially, when the majority of unknown system coefficients are zero. Convergence analysis of the proposed algorithm is presented, and a stability criterion for the algorithm is derived. This algorithm is given a name of zero‐attracting leaky‐LMS (ZA‐LLMS) algorithm. The performance of the proposed ZA‐LLMS algorithm is compared to those of the standard leaky‐LMS and ZA‐LMS algorithms in sparse system identification settings, and it shows superior performance compared to the aforementioned algorithms. Copyright © 2013 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Sparse leaky‐LMS algorithm for system identification and its convergence analysis

Salman

2013

Adaptive Control & Signal

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“…In finite impulse response (FIR) adaptive filtering, the filter weights are updated iteratively by minimizing the mean-square-error (MSE) between the desired response and the output of the adaptive filter. The well-known leastmean-square (LMS) adaptive algorithm has been used in many application areas, such as channel equalization [3], system identification [4], adaptive array processing [5], echo cancellation [6], etc. The LMS algorithm is not only simple in its filter weight updating but also reasonably fast in convergence if the optimal step-size is used [7].…”

Section: Introductionmentioning

confidence: 99%

Recursive inverse algorithm: Mean-square-error analysis

Salman

Kükrer

Hocanin

2017

Digital Signal Processing

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“…Several measures have been reported to boost up the speed of convergence. 2,3 After thorough scientific study of optimization techniques, several reasons for deviation from obtaining the optimum solution using classical optimization method have been identified. Internal structure and solution mechanism of algorithms influence the quality of solution obtained.…”

Section: Introductionmentioning

confidence: 99%

Opposition-based differential evolution for IIR system identification problem

Upadhyay

Kar

Mandal

et al. 2014

Int. J. Model. Simul. Sci. Comput.

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Nature inspired optimization algorithms have made substantial step towards solving of various engineering and scientific real-life problems. Success achieved for those evolutionary optimization techniques are due to simplicity and flexibility of algorithm structures. In this paper, optimal set of filter coefficients are searched by the evolutionary optimization technique called Opposition-based Differential Evolution (ODE) for solving infinite impulse response (IIR) system identification problem. Opposition-based numbering concept is embedded into the primary foundation of Differential Evolution (DE) technique metaphorically to enhance the convergence speed and the performance for finding the optimal solution. The population is generated with the evaluation of a solution and its opposite solution by fitness function for choosing potent solutions for each iteration cycle. With this competent population, faster convergence speed and better solution quality are achieved. Detailed and balanced search in multidimensional problem space is accomplished with judiciously chosen control parameters for mutation, crossover and selection adopted in the basic DE technique. When tested against standard benchmark examples, for same order and reduced order models, the simulation results establish the ODE as a competent candidate to others in terms of accuracy and convergence speed. Int. J. Model. Simul. Sci. Comput. 2014.05. Downloaded from www.worldscientific.com by FLINDERS UNIVERSITY LIBRARY on 02/03/15. For personal use only. 1450016-2 Int. J. Model. Simul. Sci. Comput. 2014.05. Downloaded from www.worldscientific.com by FLINDERS UNIVERSITY LIBRARY on 02/03/15. For personal use only. 1450016-4 Int. J. Model. Simul. Sci. Comput. 2014.05. Downloaded from www.worldscientific.com by FLINDERS UNIVERSITY LIBRARY on 02/03/15. For personal use only. 1450016-7 Int. J. Model. Simul. Sci. Comput. 2014.05. Downloaded from www.worldscientific.com by FLINDERS UNIVERSITY LIBRARY on 02/03/15. For personal use only. 1450016-9 Int. J. Model. Simul. Sci. Comput. 2014.05. Downloaded from www.worldscientific.com by FLINDERS UNIVERSITY LIBRARY on 02/03/15. For personal use only. 1450016-10 Int. J. Model. Simul. Sci. Comput. 2014.05. Downloaded from www.worldscientific.com by FLINDERS UNIVERSITY LIBRARY on 02/03/15. For personal use only.

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QX-LMS Adaptive FIR Filters For System Identification

Cited by 17 publications

References 4 publications

Sparse leaky‐LMS algorithm for system identification and its convergence analysis

Sparse leaky‐LMS algorithm for system identification and its convergence analysis

Recursive inverse algorithm: Mean-square-error analysis

Opposition-based differential evolution for IIR system identification problem

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