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IEEE International Conference on Signal Processing, Communications and Computing

作者： Yinxia Dong Haiquan Zhao School of Electrical Engineering Southwest Jiaotong University

ISBN: (纸本)9781479989218

In this paper, we derive a new improved proportionate normalized least mean square (IPNLMS) algorithm with unconventional minimization criterion that minimizes the summation of each squared Euclidean norm of difference between the currently updated coefficient vector and past coefficient vectors, which is called the improved IPNLMS (I-IPNLMS) algorithm. Simulation results demonstrate that the proposed I-IPNLMS algorithm has the superiority of the lower misalignment than the conventional IPNLMS algorithm in the context of sparse system identification with a low signal-noise-ratio (SNR).

关键词： Adaptive filtering Improved proportionate normalized least mean square algorithm Sparse system High measurement noise least mean square algorithm Noise measurement adaptive filters System identification Euclidean norm Summation algorithm simulation

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Normalised least-mean-square algorithm for adaptive filtering of impulsive measurement noises and noisy inputs

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ELECTRONICS LETTERS 2013年第20期49卷 1270-1271页

作者： Jung, Sang Mok Park, PooGyeon Pohang Univ Sci & Technol Dept Elect Engn Pohang Gyungbuk South Korea

A bias-compensated error-modified normalised least-mean-square algorithm is proposed. The proposed algorithm employs nonlinearity to improve robustness against impulsive measurement noise, and introduces an unbiasedne... 详细信息

关键词： Noise Noise measurement input noise adaptive filters least mean square algorithm Impulsiveness

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An efficient kernel normalized least mean square algorithm with compactly supported kernel

An efficient kernel normalized least mean square algorithm w...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： O. Toda M. Yukawa Dept. Electron. & Electr. Eng. Keio Univ. Yokohama Japan

ISBN: (纸本)9781467369985

We investigate the use of compactly supported kernels (CSKs) for the kernel normalized least mean square (KNLMS) algorithm proposed initially by Richard et al. in 2009. The use of CSKs yields sparse kernelized input vectors, offering an opportunity for complexity reduction. We propose a simple two-step method to compute the kernelized input vectors efficiently. In the first step, it computes an over-estimation of the support of the kernelized input vector based on a certain l_1-ball. In the second step, it identifies the exact support by detailed examinations based on an l_2-ball. Also, we employ the identified support given by the second step for coherence construction. The proposed method reduces the amount of l_2-distance evaluations, leading to the complexity reduction. The numerical examples show that the proposed algorithm achieves significant complexity reduction.

关键词： Compactly supported function Gaussian kernel Kernel learning Positive definite function Radial basis function least mean square algorithm Radial basis function positive definite function Seeds Kernel

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On Initial Convergence Behavior of the Kernel least mean square algorithm

On Initial Convergence Behavior of the Kernel Least Mean Squ...

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International Joint Conference on Neural Networks

作者： Badong Chen Ren Wang Nanning Zheng Jose C. Principe School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an 710049 China

ISBN: (纸本)9781479919611

The mean square convergence of the kernel least mean square (KLMS) algorithm has been studied in a recent paper [B. Chen, S. Zhao, P. Zhu, J. C. Principe, mean square convergence analysis of the kernel least mean square algorithm, Signal Processing, vol. 92, pp. 2624-2632,2012]. In this paper, we continue this study and focus mainly on the initial convergence behavior. Two measures of the convergence performance are considered, namely the weight error power (WEP) and excess mean square error (EMSE). The analytical expressions of the initial decreases of the WEP and EMSE are derived, and several interesting facts about the initial convergence are presented. An illustration example is given to support our observation.

关键词： KLMS mean square convergence weight error power (WEP) excess mean square error (EMSE) least mean square algorithm mean square Converge WEP Seeds Kernel

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Performance Analysis of least mean square Sample Matrix Inversion algorithm for Smart Antenna System

Performance Analysis of Least Mean Square Sample Matrix Inve...

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Loughborough Antennas and Propagation Conference (LAPC)

作者： Ali, Weal A. E. Mohamed, Darwish A. E. Hassan, Adel H. G. Arab Acad Sci & Technol Dept Elect & Commun Engn Alexandria Egypt

ISBN: (纸本)9781479900916

Beamforming is a signal processing technique that has the ability to direct the main beam toward signal-of-interest and the null toward signal-of-not-interest without moving antenna array. This technique is achieved by using algorithms which change the amplitude and phase of array pattern continuously. This paper presents hybrid algorithm that is a combination of two algorithms, least mean square algorithm and sample matrix inversion algorithm. The hybrid algorithm (least mean square/sample matrix inversion) is applied on an array of dipoles to overcome the shortcomings of existing algorithms for a robust smart antenna system. To focus the main beam toward the desired direction and place the null in the direction of the interference signals, the weights of the inversion matrix in sample matrix inversion are calculated and these weights will be the initial weights in least mean square algorithm. The merit of this approach solves the convergence speed problem of the least mean square algorithm as well as the computation intensive exists in sample matrix inversion algorithm and decreases the least mean square error. The simulation results indicate the analyses of least mean square, sample matrix inversion and hybrid algorithm performances. These techniques are compared and verified using MATLAB.

关键词： smart antenna least mean square algorithm sample matrix inversion algorithm Beamforming

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A Hybrid least mean square/Sample Matrix Inversion algorithm Using Microstrip Antenna Array

A Hybrid Least Mean Square/Sample Matrix Inversion Algorithm...

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Science and Information Conference (SAI)

作者： Ali, Wael A. E. Hassan, Adel H. G. Arab Acad Sci & Technol Elect & Commun Engn Dept Alexandria Egypt

ISBN: (纸本)9780989319317

Adaptive beamforming algorithms is the kind of smart antenna algorithms that have the ability to direct the main beam toward the desired signal and to place a null in the direction of the interference signal. Those algorithms achieve the desired direction by adjusting weights "amplitude and phase" of the array pattern. The proposed hybrid algorithm is presented in this paper that combines between two algorithms to improve the performance of them. This is achieved by utilizing merits of two algorithms and avoiding demerits of them. Those two algorithms are least mean square algorithm and sample matrix inversion algorithm. A hybrid algorithm of least mean square and sample matrix inversion (least mean square/sample matrix inversion) is applied on rectangular microstrip antenna to indicate the performance of the three algorithms. The microstrip antenna covers the range between 1700-1950MHz with resonant frequency 1850MHz. This frequency range is used in many applications such as mobile communication in GSM, IS-95 (CDMA), 3G, and 4G. the conventional algorithms and the hybrid algorithm are compared and verified using MATLAB.

关键词： smart antenna least mean square algorithm sample matrix inversion algorithm beamforming

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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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Application of least mean square algorithms to spacecraft vibration compensation

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JOURNAL OF THE ASTRONAUTICAL SCIENCES 1998年第1期46卷 83-90页

作者： Woodard, SE Nagchaudhuri, A NASA Langley Res Ctr Hampton VA 23665 USA Duke Univ Durham NC 27706 USA

This paper describes the application of the least mean square (LMS) algorithm in tandem with the Filtered-X least mean square algorithm for controlling a science instrument's line-of-sight pointing. Pointing error is caused by a periodic disturbance and spacecraft vibration. A least mean square algorithm is used on-orbit to produce the transfer function between the instrument's servo-mechanism and error sensor. The result is a set of adaptive transversal filter weights tuned to the transfer function. The Filtered-X LMS algorithm, which is an extension of the LMS, tunes a set of transversal filter weights to the transfer function between the disturbance source and the servo-mechanism's actuation signal. The servo-mechanism's resulting actuation counters the disturbance response and thus maintains accurate science instrumental pointing. A simulation model of the Upper Atmosphere Research Satellite is used to demonstrate the algorithms.

关键词： Lms Activation periodic disturbance transversal filters least mean square Spacecraft least mean square algorithm research satellites TRANSFER FUNCTIONS POINTING ERRORS error sensor

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Adaptive Modified Versoria Zero Attraction least mean square algorithms

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2020年第12期67卷 3602-3606页

作者： Bhattacharjee, Sankha Subhra Ray, Dwaipayan George, Nithin, V IIT Gandhinagar Dept Elect Engn Gandhinagar 382355 India Qualcomm Bangalore Design Ctr Dept QCT Platform Architecture India Bengaluru 560066 India

Low complexity and ease of implementation provided by zero-attraction-based least mean square (LMS) algorithms have made them popular candidates for sparse system identification. In this brief, a new sparsity aware norm based on a modified Versoria function is proposed, and utilized to develop a novel Versoria zero-attraction LMS (VZA-LMS) algorithm. Moreover, to make the proposed algorithm independent of the zero-attraction parameter, an adaptive zero attractor VZA-LMS (A-VZA-LMS) algorithm is also derived. The bound on learning rate, which ensures convergence in the mean and mean square sense, is derived. Computational complexity of the proposed algorithms are also compared with the existing zero-attraction-based LMS algorithms. Simulation results show that the proposed algorithms provide improved performance over the state-of-the-art algorithms.

关键词： Circuits and systems Convergence Computational complexity Adaptation models Standards Adaptive systems Versoria function sparse system identification least mean square algorithm zero attraction and low complexity

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Hybrid Nelder-Mead search based optimal least mean square algorithms for heart and lung sound separation

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2017年第3期20卷 1054-1065页

作者： Nersisson, Ruban Noel, Mathew M. VIT Univ Sch Elect Engn Vellore Tamil Nadu India

algorithms for separation of heart sounds from background lung sound noises are vital for accurate diagnosis of heart diseases. In this paper, an improved adaptive noise cancellation technique based on the least mean square (LMS) algorithm is used to separate heart sounds from lung sounds. The step size parameter in the LMS algorithm is optimally chosen using a hybrid Nelder-Mead (H-NM) optimization algorithm. The NM algorithm is initialized with a good initial solution by using computationally cheap random search to compute a rough estimate of the global minimum. Initialization of the NM algorithm with a good initial guess avoided convergence to shallow local minima and improved the quality of the final solution. The effects of using two state-of-the-arts biologically inspired heuristic optimization algorithms instead of the H-NM algorithm and three variants of the standard LMS algorithm are investigated. The correlation coefficient between the ideal and filtered heart sound signal and running time-complexity of different algorithms are taken as the metric for comparison of different heart sound separation approaches. Simulation results indicate that the approach presented in this paper performs significantly better than a variety of alternate approaches on heart sound separation problems. (C) 2017 Karabuk University. Publishing services by Elsevier B.V.

关键词： Heart sound separation Nelder-Mead search least mean square algorithm Adaptive noise cancellation Biologically inspired optimization algorithms Biological signals

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