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2017 22nd International Conference on Digital Signal Processing (DSP)

作者： Flores, Andre de Lamare, Rodrigo C. Pontificia Univ Catolica Rio de Janeiro Ctr Telecommun Studies CETUC Rio de Janeiro Brazil Univ York Dept Elect Engn York N Yorkshire England

ISBN: (纸本)9781538618950

In this paper, we propose sparsity-aware data-selective adaptive filtering algorithms with adjustable penalties. Prior work incorporates a penalty function into the cost function used in the optimization that originates the algorithms to improve their performance by exploiting sparsity. However, the strength of the penalty function is controlled by a scalar that is often a fixed parameter. In contrast to prior work, we develop a framework to derive algorithms that automatically adjust the penalty function parameter and the step size to achieve a better performance. Simulations for a system identification application show that the proposed algorithms outperform in convergence speed existing sparsity-aware algorithms.

关键词： Adaptive filtering sparsity-aware algorithms set-membership algorithms

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set-membership adaptive kernel NLMS algorithms: Design and analysis

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SIGNAL PROCESSING 2019年 154卷 1-14页

作者： Flores, Andre de Lamare, Rodrigo C. Pontificia Univ Catolica Rio de Janeiro Ctr Telecommun Studies CETUC Rio de Janeiro Brazil Univ York Dept Elect Engn York N Yorkshire England

In the last decade, a considerable research effort has been devoted to developing adaptive algorithms based on kernel functions. One of the main features of these algorithms is that they form a family of universal approximation techniques, solving problems with nonlinearities elegantly. In this paper, we present data-selective adaptive kernel normalized least-mean square (KNLMS) algorithms that can increase their learning rate and reduce their computational complexity. In fact, these methods deal with kernel expansions, creating a growing structure also known as the dictionary, whose size depends on the number of observations and their innovation. The algorithms described herein use an adaptive step-size to accelerate the learning and can offer an excellent tradeoff between convergence speed and steady state, which allows them to solve nonlinear filtering and estimation problems with a large number of parameters without requiring a large computational cost. The data-selective update scheme also limits the number of operations performed and the size of the dictionary created by the kernel expansion, saving computational resources and dealing with one of the major problems of kernel adaptive algorithms. A statistical analysis is carried out along with a computational complexity analysis of the proposed algorithms. Simulations show that the proposed KNLMS algorithms outperform existing algorithms in examples of nonlinear system identification and prediction of a time series originating from a nonlinear difference equation. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Adaptive algorithms set-membership algorithms Data-selective techniques Kernel methods Statistical analysis

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set-membership KERNEL ADAPTIVE algorithms

SET-MEMBERSHIP KERNEL ADAPTIVE ALGORITHMS

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

作者： Flores, Andre de Lamare, Rodrigo C. Pontificia Univ Catolica Rio de Janeiro CETUC Rio De Janeiro Brazil Univ York Dept Elect York N Yorkshire England

ISBN: (纸本)9781509041176

Adaptive algorithms based on kernel structures have been a topic of significant research over the past few years. The main advantage is that they form a family of universal approximators, offering an elegant solution to problems with nonlinearities. Nevertheless, these methods deal with kernel expansions, creating a growing structure also known as dictionary, whose size depends on the number of new inputs. In this paper, we derive the set-membership kernel-based normalized least-mean square (SM-NKLMS) algorithm, which is capable of limiting the size of the dictionary created in stationary environments. We also derive as an extension the set-membership kernel-based affine projection (SM-KAP) algorithm. Finally, several experiments are presented to compare the proposed SM-NKLMS and SM-KAP algorithms to existing methods.

关键词： Kernel methods sparsification set-membership algorithms kernel adaptive filtering

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set-membership kernel adaptive algorithms

Set-membership kernel adaptive algorithms

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Andre Flores Rodrigo C. de Lamare CETUC Pontifical Catholic University of Rio de Janeiro Brazil

ISBN: (纸本)9781509041183

关键词： Kernel methods sparsification set-membership algorithms kernel adaptive filtering

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Robust set-membership Affine-Projection Adaptive-Filtering Algorithm

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2012年第1期60卷 73-81页

作者： Bhotto, Md. Zulfiquar Ali Antoniou, Andreas Univ Victoria Dept Elect & Comp Engn Victoria BC V8W 3P6 Canada

An improved set-membership affine-projection (AP) adaptive-filtering algorithm is proposed. The new algorithm uses two error bounds that are estimated during the learning phase and by this means significantly reduced steady-state misalignment is achieved as compared to those in the conventional AP and set-membership AP algorithms while achieving similar convergence speed and re-adaptation capability. In addition, the proposed algorithm offers robust performance with respect to the error bound, projection order, impulsive-noise interference, and in tracking abrupt changes in the underlying system. These features of the proposed algorithm are demonstrated through extensive simulation results in system-identification and echo-cancellation applications.

关键词： Affine-projection (AP) algorithms robust adaptive-filtering algorithms set-membership algorithms

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Transient Analysis of the set-membership LMS Algorithm

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IEEE COMMUNICATIONS LETTERS 2021年第4期25卷 1298-1302页

作者： Siqueira, Newton N. Ferreira, Tiago P. Resende, Leonardo C. Pimenta, Rodrigo M. S. Haddad, Diego B. Petraglia, Mariane R. CEFET RJ BR-26041271 Nova Iguacu Brazil PUC RJ BR-22541041 Rio De Janeiro Brazil IFRJ BR-26600000 Paracambi Brazil CEFET RJ BR-20271110 Maracana Brazil CEFET RJ BR-25620003 Petropolis RJ Brazil Univ Fed Rio de Janeiro Programa Engn Elect COPPE BR-21941901 Rio de Janeiro Brazil

Adaptive filtering algorithms, which adopt the set-membership strategy, are able to attain good steady-state performance with low computational burden. In general, such advantages are obtained by defining a bounded-error. This specification translates into a time-variant step size, chosen in each iteration according to a nonlinear function of the instantaneous error. Unfortunately, this type of nonlinear behaviour hampers the stochastic modelling of these algorithms. This work devises a novel transient analysis of the set-membership Least Mean Squares algorithm. Additionally, a new interpretation is advanced about the implicit optimization problem solved by the algorithm. This explanation is important since it can contribute to the design of new adaptive algorithms. The proposed theoretical analysis provides predictions for: (i) steady-state performance;(ii) transient performance;and (iii) evolution of the update probability. It is noteworthy that the latter influences the computational complexity of the algorithm. Furthermore, we perform a novel comprehensive transient analysis of a set-membership algorithm. In addition, both time-variant transfer functions and deficient-length configurations are addressed. The resulting theoretical estimates are confirmed by simulations.

关键词： Transient analysis Prediction algorithms Mathematical model Steady-state Optimization Adaptation models Stochastic processes Adaptive filters least mean squares set-membership algorithms

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set-membership Proportionate NSAF Algorithm with Individual Activation Factors 3

Set-Membership Proportionate NSAF Algorithm with Individual ...

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3rd IEEE International Conference on Computer and Communications (ICCC)

作者： Dong, Yinxia Zheng, Zongsheng Guiyang Vocat & Tech Coll Dept Rail Transportat Guiyang Guizhou Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9781509063529

This paper presents a new set-membership proportionate normalized subband adaptive filter (SM-PNSAF) algorithm, named IAF-SM-PNSAF algorithm, by assigning an individual-activation-factor for each filter coefficient instead of a common one for all filter coefficients as in the standard SM-PNSAF. The proposed IAF-SM-PNSAF algorithm obtains a large improvement in terms of the convergence rate and tracking capability for highly sparse system as compared to the NSAF, PNSAF, SM-PNSAF and SM-IPNSAF algorithms. Simulation results verify the superiority of the proposed IAF-SM-PNSAF algorithm.

关键词： proportionate normalized subband adaptive filter individual-activation-factor set-membership algorithms sparse impulse responses

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A Family of Shrinkage Adaptive-Filtering algorithms

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2013年第7期61卷 1689-1697页

作者： Bhotto, Md. Zulfiquar Ali Antoniou, Andreas Univ Victoria Dept Elect & Comp Engn Victoria BC V8W 3P6 Canada

A family of adaptive-filtering algorithms that uses a variable step size is proposed. A variable step size is obtained by minimizing the energy of the noise-free a posteriori error signal which is obtained by using a known L-1-L-2 minimization formulation. Based on this methodology, a shrinkage affine projection (SHAP) algorithm, a shrinkage least-mean-squares (SHLMS) algorithm, and a shrinkage normalized least-mean-squares (SHNLMS) algorithm are proposed. The SHAP algorithm yields a significantly reduced steady-state misalignment as compared to the conventional affine projection (AP), variable-step-size AP, and set-membership AP algorithms for the same convergence speed although the improvement is achieved at the cost of an increase in the average computational effort per iteration in the amount of 11% to 14%. The SHLMS algorithm yields a significantly reduced steady-state misalignment and faster convergence as compared to the conventional LMS and variable-step-size LMS algorithms. Similarly, the SHNLMS algorithm yields a significantly reduced steady-state misalignment and faster convergence as compared to the conventional normalized least-mean-squares (NLMS) and set-membership NLMS algorithms.

关键词： Adaptive-filtering algorithms affine projection algorithms least-mean-squares algorithms normalized least-mean-squares algorithms set-membership algorithms

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