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6th International Conference on Intelligent and Advanced Systems (ICIAS)

作者： Talha, S. M. Umar Hussain, Hammad Moinuddin, Muhammad SSUET Dept Telecommun Engn Main Univ Rd Karachi 75300 Pakistan King Abdulaziz Univ Ctr Excellence Intelligent Engn Syst Dept Elect & Comp Engn Jeddah Saudi Arabia

ISBN: (纸本)9781509008452

Least-Mean Square algorithm, or simply the LMS, is generally one of the most commonly used techniques for optimized solution in adaptive schemes especially in the Gaussian environment. However, least mean fourth (LMF) algorithm and its variants, such as normalized LMF (nlmf), perform better in the non-Gaussian environment. The conventional LMF algorithms usually diverge in non-Gaussian environment with dynamic input. Conventionally, regularization is archived by using a small constant compensation term in the denominator of the learning rate to protect the algorithm from divergence. This paper introduces an efficient time-varying regularized normalized least mean fourth (R-nlmf) algorithm. In the proposed algorithm, the regularization term is made time varying and gradient adaptive according to steepest descent approach. Thus the proposed algorithm adapts its learning rate according to the environment and the input signal dynamics. A similar approach, namely Generalized Normalized Gradient Descent (GNGD) algorithm, has been previously applied to the normalized least mean-square (NLMS) algorithm in the Gaussian environment. However, due to its dependence on NLMS, the GNGD algorithm performance degrades in non-Gaussian environment. In order to overcome this problem, an efficient regularized nlmf algorithm for non-Gaussian environment is proposed. The algorithm shows promising results and achieves faster convergence while maintaining lesser steady-state miss-adjustment. Simulations are carried out to support the theoretical development.

关键词： Adaptive filtering nlmf algorithm gradient adaptive learning rate regularized nlmf channel estimation

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An Efficient Regularized nlmf algorithm

An Efficient Regularized NLMF Algorithm

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International Conference on Intelligent and Advanced Systems

作者： S. M. Umar Talha Hammad Hussain Muhammad Moinuddin Department of Telecommunication Engineering Sir Syed University of Engineering and Technology (SSUET) Electrical & Computer Engineering Department Center of Excellence in Intelligent Engineering Systems King Abdulaziz University Jeddah

ISBN: (纸本)9781509008469

关键词： Adaptive filtering nlmf algorithm Gradient adaptive learning rate Regularized nlmf Channel estimation

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Convergence and steady-state analysis of the normalized least mean fourth algorithm

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DIGITAL SIGNAL PROCESSING 2007年第1期17卷 17-31页

作者： Zerguine, Azzedine King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

The normalized least mean-fourth (nlmf) algorithm is presented in this work and shown to have potentially faster convergence. Unlike the LMF algorithm, the convergence behavior of the nlmf algorithm is independent of the input data correlation statistics. Sufficient conditions for the nlmf algorithm convergence in the mean are obtained and an analysis of the steady-state performance is carried out with a new approach. The latter uses the concept of feedback and bypasses the need for working directly with the weight error covariance matrix. Simulation results obtained in a system identification scenario confirms the theoretical predictions on performance of the nlmf algorithm. (c) 2006 Elsevier Inc. All rights reserved.

关键词： LMS algorithm NLMS algorithm LMF algorithm nlmf algorithm

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A Unified Performance Analysis of the Family of Normalized Least Mean algorithms

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2014年第10期39卷 7145-7157页

作者： Moinuddin, Muhammad Zerguine, Azzedine King Abdulaziz Univ Elect & Comp Engn Dept Jeddah 21859 Saudi Arabia King Abdulaziz Univ Ctr Excellence Intelligent Engn Syst CEIES Jeddah 21859 Saudi Arabia King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

This work presents a unified performance analysis of the family of normalized least mean (NLM) algorithms under very weak assumptions. The key feature of the analysis is based on a recently proposed performance measure called effective weight deviation vector. The name is so given as it is the only component that contributes to the excess estimation error of the adaptive filter. Using this novel concept, both the steady-state analysis and the tracking analysis are presented with a unified framework for the whole family of the NLM algorithms. Thus, the derived results are valid for any normalized stochastic gradient algorithm minimizing 2pth power of the error where p is an integer value. The novelty of the analysis resides in the fact that it does not impose restrictions on the dependence between input successive regressors, the dependence among input regressor elements, the length of the adaptive filter, the distribution of noise, and filter's input. Moreover, this approach is not limited to only small step-size value, and therefore, the analysis is valid for all the values of the step size in the stable range of the NLM algorithms. Furthermore, in this analysis, both stationary and non-stationary input signal and plant scenarios are considered. Consequently, asymptotic time-averaged convergence for the mean-squared effective weight deviation, mean absolute excess estimation error, and the mean-square excess estimation error for the NLM algorithm are established for both constant and time-varying plants. Finally, a number of simulation results are carried out to corroborate the theoretical findings.

关键词： LMS algorithm LMF algorithm NLMS algorithm nlmf algorithm

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A NOVEL TRACKING ANALYSIS OF THE NORMALIZED LEAST MEAN FOURTH algorithm

A NOVEL TRACKING ANALYSIS OF THE NORMALIZED LEAST MEAN FOURT...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Moinuddin, Muhammad Zerguine, Azzedine Iqra Univ Telecommun Engn Dept Karachi Pakistan

ISBN: (纸本)9781457705397

In this work, the tracking analysis of the Normalized Least Mean Fourth (nlmf) algorithm is investigated for a random walk channel under very weak assumptions. The novelty of this work resides in the fact that no restrictions are made on the dependence between the input successive regressors, the dependence among input regressor elements, the length of the adaptive filter, the distribution of noise and filter's input. Moreover, in our approach, there is no restriction made on the step size value and therefore the analysis holds for all the values of the step size in the range of stable nlmf algorithm. The analysis is based on a recently proposed performance measure called effective weight deviation vector which is the component of weight deviation vector in the direction of input regressor. In this paper, asymptotic time-averaged convergence for the mean square effective weight deviation, mean absolute excess estimation error, and the mean square excess estimation error for the nlmf algorithm are established. Finally, a number of simulation results are carried out to corroborate the theoretical findings.

关键词： Adaptive filters nlmf algorithm Convergence Analysis

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STEADY-STATE ANALYSIS OF THE NORMALIZED LEAST MEAN FOURTH algorithm WITHOUT THE INDEPENDENCE AND SMALL STEP SIZE ASSUMPTIONS

STEADY-STATE ANALYSIS OF THE NORMALIZED LEAST MEAN FOURTH AL...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Moinuddin, Muhammad Zerguine, Azzedine King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

ISBN: (纸本)9781424423538

In this work, the steady-state analysis of the Normalized Least Meal? Fourth (nlmf) algorithm under very weak assumptions is investigated. No restrictions are made on the dependence between input successive regressors, the dependence among input regressor elements, the length of the adaptive filter the distribution of noise and the filter input. Moreover in our approach, there is no restriction made on the step size value and therefore the analysis holds for all the values of the step size in the range where the nlmf algorithm is stable. The analysis is based on the effective weight deviation vector performance measure [1]. This vector is the component of weight deviation vector in the direction of the input regressor The asymptotic time-averaged convergence for the mean square effective weight deviation, the mean absolute excess estimation error and the mean square excess estimation error for the nlmf algorithm are derived. Finally, a number of simulation results are carried out to corroborate the theoretical findings.

关键词： Adaptive filters nlmf algorithm Convergence Analysis

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Distributed Adaptive LMF algorithm for Sparse Parameter Estimation in Gaussian Mixture Noise 7

Distributed Adaptive LMF Algorithm for Sparse Parameter Esti...

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7th International Symposium on Telecommunications (IST)

作者： Hajiabadi, Mojtaba Zamiri-Jafarian, Hossein Ferdowsi Univ Mashhad Fac Engn Dept Elect Engn Mashhad Iran

ISBN: (纸本)9781479953592

A distributed adaptive algorithm for estimation of sparse unknown parameters in the presence of nonGaussian noise is proposed in this paper based on normalized least mean fourth (nlmf) criterion. At the first step, local adaptive nlmf algorithm is modified by zero norm in order to speed up the convergence rate and also to reduce the steady state error power in sparse conditions. Then, the proposed algorithm is extended for distributed scenario in which more improvement in estimation performance is achieved due to cooperation of local adaptive filters. Simulation results show the superiority of the proposed algorithm in comparison with conventional nlmf algorithms.

关键词： Cooperative estimation Gaussian mixture noise nlmf algorithm sparse parameters zero norm

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The PNLMS algorithm with Unbiasedness criterion in Sparse System identification 3

The PNLMS Algorithm with Unbiasedness criterion in Sparse Sy...

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3rd International Conference on Electronics and Communication;Network and Computer Technology (ECNCT)

作者： Song, Linzhi Zhu, Min Chen, YouXiang Wang, Li Xv, Nuo Hao, ZhiGang Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian Peoples R China Xi An Jiao Tong Univ Res Inst Suzhou 215123 Peoples R China Xi An Jiao Tong Univ Res Inst Hangzhou 311215 Zhejiang Peoples R China Air Force Engn Univ Sch Aeronaut Engn Xian Peoples R China

ISBN: (数字)9781510652118

ISBN: (纸本)9781510652118;9781510652101

The proportionate normalized least mean square (PNLMS) algorithm is used in sparse system identification for its simplicity and adaptively step-size adjusting scheme. However, the PNLMS has the noisy input and the non-Gaussian output noise problem. A bias Compensated Pnlmf algorithm (called BCPnlmf) for identifying sparse system has been proposed to solve aforementioned issues. The BCPnlmf algorithm which takes advantage of the bias compensated and the proportionate scheme, can achieve better steady-state accuracy and faster convergence speed besides identify the system parameters in noisy input and output with non-Gaussian character environments. Simulation results carried out in sparse system identification confirm the remarkable performance of the BCPnlmf, compared with other well-known algorithms.

关键词： Bias Compensated Proportionate nlmf algorithm Sparse System Identification Noisy input

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