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1st International Conference on Computational Intelligence and Information Technology (CIIT 2011)

作者： Sawale, Manish D. Yadav, Ram N. Maulana Azad Natl Inst Technol Dept Elect & Commun Bhopal India

ISBN: (纸本)9783642257339

In this paper we derive an improved minimization criterion for normalized least mean squares (NLMS) algorithm using past weight vectors and the regularization parameter. The proposed minimization criterion minimizes the mean square deviation (MSD) of currently updated weight vector and past weight vector. The result of the proposed NLMS algorithm approaches the conventional NLMS algorithm as the regularization parameter reduces to zero. The result also shows that as the regularization parameter decreases the convergence rate increases.

关键词： normalized least mean square algorithm minimization criterion regularization parameter mean square deviation and weight vector

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A fast convergence normalized least-mean-square type algorithm for adaptive filtering

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2014年第10期28卷 1073-1080页

作者： Benallal, A. Arezki, M. Univ Blida Dept Elect LATSI Lab Blida Algeria

A new adaptive algorithm with fast convergence and low complexity is presented. By using the calculation structure of the dual Kalman variables of the fast transversal filter algorithm and a simple decorrelating technique for the input signal, we obtain an algorithm that exhibits faster convergence speed and enhanced tracking ability compared with the normalized least-mean-square algorithm with similar computational complexity. Copyright (C) 2013 John Wiley & Sons, Ltd.

关键词： adaptive filters fast transversal filter algorithm normalized least mean square algorithm complexity reduction

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Stochastic analysis of the diffusion least mean square and normalized least mean square algorithms for cyclostationary white Gaussian and non-Gaussian inputs

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2021年第12期35卷 2466-2486页

作者： Eweda, Eweda Bershad, Neil J. Bermudez, Jose C. M. Future Univ Dept Elect Engn Cairo Egypt Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA USA Univ Fed Santa Catarina Dept Elect Engn Florianopolis SC Brazil

The diffusion least mean square (DLMS) and the diffusion normalized least mean square (DNLMS) algorithms are analyzed for a network having a fusion center. This structure reduces the dimensionality of the resulting stochastic models while preserving important diffusion properties. The analysis is done in a system identification framework for cyclostationary white nodal inputs. The system parameters vary according to a random walk model. The cyclostationarity is modeled by periodic time variations of the nodal input powers. The analysis holds for all types of nodal input distributions except for distributions with infinite variance. The derived models consist of simple scalar recursions. These recursions facilitate the understanding of the network mean and mean-square dependence upon the 1) nodal weighting coefficients, 2) nodal input kurtosis and cyclostationarities, 3) nodal noise powers, and 4) the unknown system mean-square parameter increments. Optimization of the node weighting coefficients is studied. Also investigated is the stability dependence of the two algorithms upon the nodal input kurtosis and weighting coefficients. Significant differences are found between the behaviors of the DLMS and DNLMS algorithms for non-Gaussian nodal inputs. Simulations provide strong support for the theory.

关键词： adaptive filters least mean square algorithm normalized least mean square algorithm stochastic diffusion algorithms

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A noise-resilient affine projection algorithm and its convergence analysis

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SIGNAL PROCESSING 2016年 121卷 94-101页

作者： Kim, Seong-Eun Lee, Jae -Woo Song, Woo-Jin MIT Dept Brain & Cognit Sci E25-618 Cambridge MA 02139 USA Pohang Univ Sci & Technol POSTECH Div Elect Engn Pohang 790784 South Korea

Recently a new normalized least mean square algorithm has been proposed by minimizing the summation of the squared Euclidean norms of the changes between the weight vectors to be updated and the past weight vector. The resultant algorithm exhibits noise resilience in that they prevent the adaptive filter from fluctuating around an optimal solution, but its convergence behavior has not been studied in detail. Thus, we first apply the constrained criterion to an affine projection algorithm (APA) for identifying a highly noisy system by reusing weight vectors. Since the performance of the APA declines under low signal-to-noise ratio (SNR) conditions, this approach is more effective for decreasing the steady-state mean-square deviation (MSD). Then, we analyze the convergence behavior of the proposed APA theoretically using energy conservation arguments. The experimental results show that the proposed theoretical results agree well with the simulation results. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Adaptive filter normalized least mean square algorithm Affine projection algorithm Weight vector reusing Energy conservation mean-square performance analysis

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Adaptive combination of affine projection and NLMS algorithms based on variable step-sizes

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DIGITAL SIGNAL PROCESSING 2016年第0期59卷 86-99页

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

Considering the filters with variable step-sizes outperform their fixed step-sizes versions and the combination algorithms with proper mixing parameters outperform their components, a combination algorithm consisting of improved variable step-size affine projection (I-VSSAP) and normalized least mean square (I-VSSNLMS) algorithms, of which the former is fast and the latter is slow, is proposed for stationary environment Different from the combination algorithms whose components are updated independently, the variable step-sizes components are adapted using the same input and error signals, and their step-sizes are derived via the mean-square deviation (MSD) of the overall filter. Therefore, the components reflect the working state of the combination filter more accurately than their fixed step sizes versions. The mixing parameter is obtained by minimizing the MSD and gradually decreases from 1 to 0. Therefore the proposed algorithm has a performance similar to I-VSSAP and I-VSSNLMS in the initial stage and steady-state respectively. Simulations confirm that the proposed algorithm outperforms its components and its fixed step-sizes version. The mixing parameter is artificially set to 0 when the difference between the MSDs of two adjacent iterations is below a user-defined threshold, then the proposed algorithm degrades to I-VSSNLMS and exhibits a less computational complexity than AP algorithm. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Affine projection algorithm normalized least mean square algorithm Variable step-size Mixing parameter mean-square deviation

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Variable step-size normalized LMS algorithm by approximating correlation matrix of estimation error

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SIGNAL PROCESSING 2010年第9期90卷 2792-2799页

作者： Cho, Hyeonwoo Kim, Sang Woo Pohang Univ Sci & Technol Div Elect & Comp Engn Pohang 790784 Gyungbuk South Korea

In this letter, we propose a variable step-size normalized least mean square (NLMS) algorithm. We study the relationship among the NLMS, recursive least square and Kalman filter algorithms. Based on the relationship, we derive an equation to determine the step-size of NLMS algorithm at each time instant. In steady state, the convergence of the proposed algorithm is verified by using the equation, which describes the relationship among the mean-square error, excess mean-square error, and measurement noise variance. Through computer simulation results, we verify the performance of the proposed algorithm and the change in the variable step-size over iterations. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Variable step-size normalized least mean square algorithm Kalman filter Recursive least square State-space equation

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Performance evaluation of a new variable tap-length learning algorithm for automatic structure adaptation in linear adaptive filters

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2015年第1期69卷 253-261页

作者： Kar, Asutosh Chandra, Mahesh IIIT Dept Elect & Telecommun Engn Bhubaneswar Orissa India BIT Dept Elect & Commun Engn Mesra India

In this work, a variable-tap length, variable step normalized least mean square algorithm with variable error spacing is proposed. The algorithm finds the optimized tap-length that best balances the complexity and steady state performance in linear adaptive filters. The design provides a systematic procedure with mathematical analysis to select the variable key parameters that affect the structure adaptation. The proposed structure adaptation algorithm maintains a trade-off between the mean square error and convergence speed. A sliding window weight update method is presented along with the tap-length learning algorithm to reduce the structural as well as computational complexity. Guidelines for parameter selection to formulate the optimum tap-length in correspondence with the designed algorithm are shown and assumptions are specified. The proposed algorithm has performed better than the existing fractional tap-length learning methods for both low and high noise conditions. This is achieved because of the unique method adopted in this paper to set dynamic system independent parameters instead of predefined fixed settings. (C) 2014 Elsevier GmbH. All rights reserved.

关键词： Adaptive filters Variable tap-length normalized least mean square algorithm mean square error Fractional tap-length

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Adaptive Price Estimation in Cognitive Radio Enabled Smart Grid Networks

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第2期46卷 1451-1463页

作者： Das, Deepa Khadanga, Rajendra Kumar GCE Dept Elect Engn Kalahandi India NIST Dept Elect Engn Berhampur India

This paper focuses on the allocation of resources to cognitive users in a two-way cognitive radio facilitated smart grid (SG) network. The resources are essential in controlling the performance of demand response management in the SG, ensuring profit to the power supplier and simultaneously cost-saving to the consumers. However, cognitive users need more power for their data transmission, which compels the utility company to increase its electricity price. Hence, we propose an adaptive resource allocation algorithm based on normalized least mean squares (NLMS) to estimate the electricity price that benefits both the supplier and consumers with optimal allocation of power demands under the constraints of transmission power, system throughput, and the probability of detection. The simulation results validate the performance of our proposed scheme by comparing it with the application of metaheuristic algorithms for maximizing aggregate profit. The impact of the channel parameters on system performance is also studied.

关键词： Smart grid Demand response management Cognitive radio normalized least mean square algorithm

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A New Method of Adaptive Filtering and Wavelet Transform to Filter Baseline Shift

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JOURNAL OF INFORMATION TECHNOLOGY RESEARCH 2018年第3期11卷 119-134页

作者： Shi, Jianting Wang, Jiancai Heilongjiang Univ Sci & Technol Coll Comp & Informat Engn Harbin Heilongjiang Peoples R China Heilongjiang Univ Sci & Technol Off Acad Affairs Harbin Heilongjiang Peoples R China

This article describes how a baseline shift is a slow change in the orientation of the baseline over time. It often exists in the process of signals sampling, e g. ECG, TLC and so on. In order to filter the baseline shift, a combination method of wavelet transform and an adaptive filter is proposed. First, the wavelet transform method is used to decompose the original ECG signal and the high-frequency components are used to as Reference input data. Then, a new adaptive filtering algorithm, P-LMS, based on the power function, is proposed to conduct adaptive noise filtering. Finally, compared with the traditional normalized least mean square algorithm (NLMS), the proposed algorithm has the characteristics of faster convergence and the effect is better. Experiments on the ECG signal in MIT-BIH database, using the method of combining P-LMS and a wavelet transform is verified to effectively filter the baseline shift and maintain the geometric characteristics of the ECG signal.

关键词： Adaptive filter Baseline shift High-frequency Noise normalized least mean square algorithm Power function Signals sampling Wavelet transform

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normalized LMS digital filter for chromatic dispersion equalization in 112-Gbit/s PDM-QPSK coherent optical transmission system

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OPTICS COMMUNICATIONS 2010年第6期283卷 963-967页

作者： Xu, Tianhua Jacobsen, Gunnar Popov, Sergei Li, Jie Wang, Ke Friberg, Ari T. Royal Inst Technol SE-16440 Stockholm Sweden Acreo AB SE-16440 Kista Sweden Tianjin Univ Tianjin 300072 Peoples R China

High bit rates optical communication systems pose the challenge of their tolerance to linear and nonlinear fiber impairments. Coherent optical receivers using digital signal processing techniques can mitigate the fiber impairments in the optical transmission system, including the chromatic dispersion equalization with digital filters. In this paper, an adaptive finite impulse response filter employing normalized least mean square algorithm is developed for compensating the chromatic dispersion in a 112-Gbit/s polarization division multiplexed quadrature phase shift keying coherent communication system, which is established in the VPI Simulation platform. The principle of the adaptive normalized least mean square algorithm for signal equalization is analyzed theoretically, and at the meanwhile, the taps number and the tap weights in the adaptive finite impulse response filter for compensating a certain fiber chromatic dispersion are also investigated by numerical simulation. The chromatic dispersion compensation performance of the adaptive filter is analyzed by evaluating the behavior of the bit-error-rate versus the optical signal-to-noise ratio, and the compensation results are also compared with other present digital filters. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Coherent optical receiver Digital signal processing Chromatic dispersion equalization Polarization division multiplexed quadrature phase shift keying normalized least mean square algorithm Adaptive finite impulse response filter

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