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Posterior Error Energy Minimization Based Combined FxNLMS and FxLMS Algorithm for Active Noise Control

IEEE ACCESS 2024年 12卷 30754-30764页

作者： Pang, Yunhe Ou, Shifeng Cai, Zhuoran Gao, Ying Yantai Univ Sch Phys & Elect Informat Yantai 264005 Peoples R China

The filtered-x normalized least mean square (FxNLMS) algorithm is widely used in active noise control (ANC) systems. However, for cyclostationary input signals, the noise reduction performance of the FxNLMS algorithm is affected by the periodic power of cyclostationary inputs, which leads to a larger steady-state mean square deviation (MSD) than the filtered-x least mean squares (FxLMS) algorithm. In this paper, a new adaptive combination algorithm of FxNLMS and FxLMS is proposed to suppress cyclostationary input noise with fast convergence and low steady-state MSD. The mixing parameter for combining the two algorithms is obtained by minimizing the energy of the posterior error and updated using the time-average method. The mathematical analysis including the mean square convergence and computational complexity are performed. The simulation results show that the proposed combined-FxNLMS-FxLMS algorithm can efficiently combine the respective advantages of FxNLMS and FxLMS algorithms and improve the noise reduction performance of the ANC system.

关键词： Filtering algorithms Convergence Adaptive filters Steady-state Vectors Noise reduction Approximation algorithms Noise measurement parameter estimation Least mean squares methods Mathematical models Performance evaluation Cyclostationary process Active noise control adaptive combinations mixing parameter moving average

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An adaptive convex combination of CLMK and ACLMK algorithms for processing complex-valued signals

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SIGNAL PROCESSING 2023年 205卷

作者： Guvenc, Buket Colak Menguc, Engin Cemal Kayseri Univ Fac Engn Architecture & Design Dept Elect & Elect Engn TR-38280 Talus Kayseri Turkiye

This paper introduces a novel adaptive convex combination (ACC) of the complex-valued least mean kur-tosis (CLMK) and augmented CLMK (ACLMK) algorithms by inspiring their individual advantages to ef-fectively process circular and noncircular complex-valued signals. For this purpose, the negated kurtosis -based cost function of the overall error signal in the complex domain is primarily defined and then it is minimized to obtain the update rule of the mixing parameter for the proposed ACC. The proposed ACC offers a significant opportunity for the CLMK and ACLMK algorithms to work collaboratively. By it-eratively updating the mixing parameter based on the mentioned kurtosis cost function, the proposed ACC adaptively dominates the CLMK or ACLMK at its overall output according to the requirements of the filter operating environments to improve the overall filtering performance in terms of the convergence rate and steady-state error. The evolution of the mixing parameter in the proposed ACC also gives in-formation about the characteristic behaviour of the considered signal or system. Moreover, for the first time in this paper, the stability of the mixing parameter update rule of the proposed ACC is theoretically analysed in the sense of Lyapunov to derive its step size bounds. On the other h and, since the analyses of the CLMK and ACLMK algorithms in the sense of Lyapunov are still missing in the literature, in the scope of this work, their Lyapunov analyses are also provided in detail. The comprehensive simulations on system identification and one-step ahead prediction scenarios in the complex domain support both the mentioned elegant properties of the proposed ACC and its superior performance over the existing ACC. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Adaptive convex combination Circular and noncircular complex-valued signals Least mean kurtosis mixing parameter Lyapunov stability

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Quantization for Decentralized Learning Under Subspace Constraints

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 2320-2335页

作者： Nassif, Roula Vlaski, Stefan Carpentiero, Marco Matta, Vincenzo Antonini, Marc Sayed, Ali H. Univ Cote dAzur I3S Lab CNRS F-06000 Nice France Imperial Coll London London SW7 2BX England Univ Salerno I-84084 Fisciano Italy Swiss Fed Inst Technol Lausanne Inst Elect & Micro Engn CH-1015 Lausanne Switzerland

In this article, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained formulation includes consensus or single-task optimization as special cases, and allows for more general task relatedness models such as multitask smoothness and coupled optimization. In order to cope with communication constraints, we propose and study an adaptive decentralized strategy where the agents employ differential randomized quantizers to compress their estimates before communicating with their neighbors. The analysis shows that, under some general conditions on the quantization noise, and for sufficiently small step-sizes mu, the strategy is stable both in terms of mean-square error and average bit rate: by reducing mu, it is possible to keep the estimation errors small (on the order of mu) without increasing indefinitely the bit rate as mu -> 0 when variable-rate quantizers are used. Simulations illustrate the theoretical findings and the effectiveness of the proposed approach, revealing that decentralized learning is achievable at the expense of only a few bits.

关键词： Stochastic optimization decentralized subspace projection differential quantization randomized quantizers stochastic performance analysis mixing parameter decentralized learning decentralized optimization

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A Collaborative Spline Adaptive Filter for Nonlinear Echo Cancellation

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2021年第4期40卷 1699-1719页

作者： Zhao, Yi-Bo Yan, Tao Chen, Wen-Yuan Lu, Hao-Zhi Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Peoples R China

Acoustic echo causes the quality of communication to degenerate and results in loss of clarity. Though conventional linear adaptive filters have been applied successfully to eliminate linear acoustic echo, they can barely deal with the problem of nonlinear acoustic echo. In this paper, a collaborative spline adaptive filter is presented to eliminate nonlinear acoustic echo. The filter is composed of a linear adaptive filter in the upper branch and a nonlinear spline adaptive filter in the lower branch. The nonlinear one is a Hammerstein system consisting of a spline interpolation function and a subsequent linear adaptive filter. The two branches are collaboratively combined and a mixing parameter is adopted, which can be updated to adjust the proportion of the lower branch output signal. Experimental results show that the presented method can achieve a good performance in nonlinear echo cancelation regardless of whether the nonlinear degree of the echo path varies with time or not.

关键词： Collaborative spline adaptive filter Nonlinear acoustic echo Hammerstein system mixing parameter

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Combined regularization parameter for normalized LMS algorithm and its performance analysis

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SIGNAL PROCESSING 2019年 162卷 75-82页

作者： Shi, Long Zhao, Haiquan Wang, Wenyuan Lu, Lu Minist Educ Key Lab Magnet Suspens Technol & Maglev Vehicle Beijing Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Sichuan Peoples R China Sichuan Univ Sch Elect & Informat Engn Chengdu 610065 Sichuan Peoples R China

This paper presents a combined regularization parameter for normalized least-mean-square (CRP-NLMS) algorithm. The proposed algorithm adaptively combines two different regularization parameters by employing a time-varying mixing parameter that is derived by minimizing the energy of noise-free a posteriori error. To avoid large fluctuations, the mixing parameter is updated in a moving-average method. A novel reset method is designed to improve the tracking capability when the unknown system encounters a sudden change. We illustrate that the proposed mixing parameter is also available for the nonstationary system modeled by a random walk process. In particular, the theoretical analyses including the transient and steady-state mean-square error (MSE) are performed. Simulations for system identification scenarios demonstrate the merits of Kour finding and support the theoretical analysis. (C) 2019 Elsevier B.V. All rights reserved.

关键词： A posteriori error Combined regularization parameter mixing parameter Moving-average

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Pranav Quasi Gamma Distribution: Properties and Applications

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JOURNAL OF STATISTICAL THEORY AND APPLICATIONS 2020年第4期19卷 506-517页

作者： Wani, Sameer Ahmad Hassan, Anwar Shafi, Shaista Shafi, Sumeera Univ Kashmir Dept Stat Srinagar 190006 India Univ Kashmir Dept Math Srinagar 190606 India

We have developed Pranav Quasi Gamma Distribution (PQGD) as a mixture of Pranav distribution (theta) and Quasi Gamma distribution (2, theta). We obtained various necessary statistical characteristics of PQGD. The flexibility of proposed model is clear from graph of hazard function. The reliability measures of proposed model are also obtained. Sample estimates of unknown parameters are obtained by making use of maximum likelihood estimation method. We have also carried out the simulation study for comparing our model with its related models. We then tested the significance of mixing parameter. Finally, applications to real-life data sets is presented to examine the significance of newly introduced model. (C) 2020 The Authors. Published by Atlantis Press B.V.

关键词： Quasi gamma distribution Pranav distribution Mixture models Simulation study mixing parameter Structural properties and maximum likelihood estimation

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A Normalized Subband Adaptive Filter with Combined Regularization parameter 5th

A Normalized Subband Adaptive Filter with Combined Regulariz...

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5th IFAC Symposium on Telematics Applications (TA)

作者： Shi, Long Zhao, Haiquan Southwest Jiaotong Univ Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 610031 Sichuan Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Sichuan Peoples R China

Proposed is a novel variable regularization algorithm for normalized subband adaptive filter (NSAF) by employing the mixing parameter to combine a large regularization parameter with a small regularization parameter. The mixing parameter is derived by minimizing the energy of noise-free a posteriori subband error. The combined regularization parameter can vary from a small value to a large value. Therefore, the proposed algorithm exhibits fast convergence rate meanwhile obtains low steady-state misalignment. Experimental simulations demonstrate the effectiveness of our work. Copyright (C) 2019. The Authors. Published by Elsevier Ltd. All rights reserved.

关键词： subband adaptive filter mixing parameter regularization parameter fast convergence misalignment

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A Normalized Subband Adaptive Filter with Combined Regularization parameter

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IFAC-PapersOnLine 2019年第24期52卷 158-162页

作者： Long Shi Haiquan Zhao The Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle Ministry of Education and the School of Electrical Engineering Southwest Jiaotong University Chengdu 610031 China

关键词： subband adaptive filter mixing parameter regularization parameter fast convergence misalignment

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Adaptive Combination of Distributed Incremental Affine Projection Algorithm with Different Projection Orders

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2018年第10期37卷 4319-4335页

作者： Shi, Long Zhao, Haiquan Minist Educ Key Lab Magnet Suspens Technol & Maglev Vehicle Chengdu Sichuan Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu Sichuan Peoples R China

Recently, the distributed incremental affine projection algorithm (DIAPA) has attracted much attention owing to its good performance for correlated input. However, the DIAPA algorithm with high projection order achieves fast convergence rate but suffers from large steady-state misalignment, and that with low projection order has small steady-state misalignment, but it converges slowly. To overcome this trade-off, in this brief, an adaptive combination of the distributed incremental affine projection algorithm with different projection orders (ACDIAPA-DPO) is proposed, which combines the DIAPA using high projection order with that using low projection order by an adaptive mixing parameter. The mixing parameter is obtained by minimizing the mean square deviation. We discuss the computational complexity of the proposed algorithm and other existing algorithms. Moreover, a novel re-initialization mechanism is introduced to further improve the tracking capability of the ACDIAPA-DPO when the system suddenly changes. Simulations results over the incremental network show the superiority of the proposed algorithm.

关键词： Incremental affine projection Adaptive combination mixing parameter Re-initialization mechanism

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A New Data-Reusing Algorithm Based on Minimum Norm and Minimum Disturbance Principles

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2017年第5期36卷 1948-1969页

作者： Ren, Chunhui Wang, Zuozhen Zhao, Zhiqin Univ Elect Sci & Technol China Chengdu 611731 Sichuan Peoples R China

Aiming to accelerate convergence speed and reduce steady-state misalignment of adaptive filter, a new data-reusing algorithm is proposed. Different from conventional affine projection algorithm (APA) based on minimum disturbance principle (MDP), the proposed algorithm obtains its cost function based on a convex combination of the minimum norm principle (MNP) and the MDP. Weight factor (named as mixing parameter) used in the combination determines the approach for updating. The proposed algorithm has a performance similar to the APA when the mixing parameter closes to 0 and yields results to the data-reusing algorithm based on the MNP if the mixing parameter closes to 1. By minimizing mean-square deviation, the variable mixing parameter has a large value at initial stage and gradually decreases as iteration number increases. At the initial stage, the proposed algorithm achieves fast convergence since the MNP plays a leading role. At the steady-state, the proposed algorithm obtains a low misalignment since the MDP gives a major impact and the updated results have minimum disturbance from the previous ones. At the transition stage, the proposed algorithm is a combination of the APA and the MNP-based data-reusing algorithm. Simulation results show that the proposed algorithm exhibits faster convergence rate and lower steady-state misalignment than some other derivatives of the APA without significantly increasing computational complexity.

关键词： Adaptive filters Date-reusing Minimum norm Minimum disturbance principle mixing parameter Mean-square deviation

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