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Exact analysis of the least-mean-square algorithm with coloured measurement noise

ELECTRONICS LETTERS 2018年第24期54卷 1401-1402页

作者： Lara, Pedro Olinto, Karen da S. Petraglia, Felipe R. Haddad, Diego B. CEFET RJ Rio De Janeiro RJ Brazil Univ Fed Rio de Janeiro Rio de Janeiro RJ Brazil

In general, theoretical analyses of adaptive filtering algorithms employ statistical approximations in order to render the derivations tractable. Among such hypotheses, the statistical independence between the current adaptive coefficients and past input vectors is a very popular one. Unfortunately, this simplification gives rise to discrepancies with respect to empirical results, especially for large values of the step-size parameter. In this Letter, this issue is overcome by the usage of an exact expectation analysis (i.e. a stochastic model that does not employ the above-mentioned independence assumption) of the least-mean-squares adaptive algorithm. The authors analysis is also generalised in order to address the common case of coloured additive noise, an issue that is so far missing from the literature. The accuracy of the advanced model is verified through simulations.

关键词： least mean squares methods adaptive filters filtering theory stochastic processes statistical independence current adaptive coefficients input vectors simplification empirical results step-size parameter exact expectation analysis stochastic model above-mentioned independence assumption least-mean-squares authors analysis coloured additive noise exact analysis least-mean-square algorithm coloured measurement noise general analyses theoretical analyses adaptive filtering algorithms statistical approximations derivations tractable

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Distributed Estimation with Novel Adaptive Data Selection Based on a Cross-Matching Mechanism

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2023年第10期42卷 6324-6346页

作者： Wan, Fangyi Hua, Yi Liao, Bin Ma, Ting Qing, Xinlin Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China Xiamen Univ Sch Aerosp Engn Xiamen 361000 Peoples R China

Distributed estimation using general data selection (DS) has always been applicable for reducing calculation loads in many fields. However, the traditional general DS (GDS) mode can deteriorate algorithm performance and usually neglects solving the problem of communication cost. These issues arise because distributed estimation is extremely susceptible to selecting the fused data and requires swapping all data. To solve these problems, a diffusion least-mean-square (DLMS) algorithm with an adaptive DS (ADS) is proposed to improve the GDS mode. The proposed algorithm can choose more reliable information in the data fusion process and diminish the communication cost (by using the saved intermediate data of previous iteration) and the calculation load. In addition, in GDS mode, the DS factor (DSF) selects data based on noise statistics (NS), resulting in some loss of selection ability. To further improve this situation, a novel cross-matching mechanism is proposed to improve the design of the DSF based on an intermediate estimation error. The mean stability and mean-square performance of the proposed DLMS algorithm with the ADS mode are analyzed theoretically, which can derive a convergence condition based on the step-size. Theoretical verification and target localization simulations are implemented to illustrate the effectiveness and robustness of the proposed ADS algorithm under satisfying the convergence condition as compared to other related DS algorithms.

关键词： Adaptive data selection Distributed estimation least-mean-square algorithm Cross-matching mechanism

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Comparative Performance Study of DVR Using Adaptive LMS Filtering-Based algorithms

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2024年第7期52卷 1054-1081页

作者： Kassarwani, Neelam Ohri, Jyoti Singh, Alka NIT Kurukshetra Elect Engn Kurukshetra India Delhi Technol Univ Elect Engn Delhi India

In the distribution system, voltage sags and swell have adverse effects on sensitive and critical loads. Dynamic Voltage Restorer (DVR) play a vital role to mitigate voltage sags and swell through its organized control when connected in series with the system. Various conventional control schemes have been reported for the control of DVR. The performance of DVR with these schemes has been successful in the mitigation of voltage sags/swell, but drawbacks, such as undershoot, and overshoot during sag dynamics are observed in the regulated load terminal voltage. These drawbacks may cause malfunctioning of the equipment connected and hence their elimination is indispensable. With this inspiration, a novel Adaline filter-based adaptive control scheme using learning-rate parameter-based least-mean-square algorithm has been proposed. This scheme has the capability to overcome the drawbacks with the conventional control schemes thus improving the performance of DVR. In this paper, the performance of DVR using proposed algorithm is compared with that using synchronous reference frame (SRF)-based conventional control scheme. The performance is studied and validated through simulation results under different voltage sag and swell conditions in MATLAB software using Sim Power System toolboxes.

关键词： voltage sag dynamic voltage restorer synchronous reference frame algorithm adaptive control learning-rate parameter least-mean-square algorithm Adaline filter

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A Modified RLS algorithm for ICA with Weighted Orthogonal Constraint

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2020年第6期39卷 3046-3060页

作者： Jianwei, E. Ye, Jimin Xidian Univ Sch Math & Stat Xian 710071 Shaanxi Peoples R China

Independent component analysis (ICA), as an important data processing technique, is widely employed in many areas. The objective of the ICA is to recover independent components from observed signals. Several algorithms, such as equivariant adaptive separation via independence algorithm, least-mean-square (LMS)-type algorithms and recursive least-squares (RLS)-type learning rules, are proposed to solve the ICA problem. In the present paper, a modified RLS algorithm for ICA with weighted orthogonal constraint is developed to implement source separation based on the local convergence analysis of the available algorithm. Comparative experiment results demonstrate that the proposed algorithm is better than existing learning rules in the aspect of the accuracy of separation and stability.

关键词： Independent component analysis least-mean-square algorithm Recursive least-squares algorithm Weighted orthogonal constraint

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Stabilization of a Modified LMS algorithm for Canceling Nonlinear Memory Effects

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 3439-3449页

作者： Xiao, Yue-Yu Shanghai Univ Sch Commun & Informat Engn Shanghai 201800 Peoples R China

The least-mean-square (LMS) is promising in a system whose signal statistics are time-varying, and it is preferred in adaptive digital predistortion techniques. In a nonlinear memory system, it is found that the high nonlinearity, the finite sampling rate and the finite number of digits will introduce colored noise that is dependent on signals. In this paper, the long-term stability of the conventional LMS in the presence of signal-dependent noise is analyzed using perturbation analysis. It is revealed that the cumulative effect of the signal-dependent noise may lead to a divergence of the conventional LMS, and a properly selected leaky coefficient is needed to suppress the divergence. Unfortunately, the compensation performance may be harmed by enlarging the leaky coefficient. To address this problem, a modified LMS (MLMS) algorithm is proposed. By adjusting the updating strategy in the iterations, the long-term stability of the algorithm is guaranteed, without harming the linearization performance. Numerical simulations verify that the MLMS outperforms the conventional LMS both in stability and compensation performance.

关键词： Predistortion Stability analysis Gain Low pass filters Signal processing algorithms Numerical stability Nonlinear distortion least-mean-square algorithm stability analysis nonlinear memory effect predistortion system

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A variable step-size incremental LMS solution for low SNR applications

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SIGNAL PROCESSING 2021年 178卷 107730-107730页

作者： Bin Saeed, Muhammad Omer Pasha, Syed Ahmed Zerguine, Azzedine Air Univ Elect & Comp Engn Dept Islamabad 44000 Pakistan King Fahd Univ Petr & Minerals Elect Engn Dept Dhahran 31261 Saudi Arabia

Many practical applications built around adaptive algorithms require solutions that work at low signal-to-noise ratio (SNR) values. In this situation, a variable step-size algorithm is a must to accommodate this scenario. A plethora of variable step algorithms exist in the literature for high SNR values. This paper presents a different variable step-size incremental least mean square strategy that provides excellent performance at low SNR values. A detailed theoretical analysis has been carried out for the proposed algorithm. Simulations have been performed for different scenarios to demonstrate the validity of the proposed strategy. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Variable step-size least-mean-square algorithm Incremental scheme Low SNR mean-square analysis Steady-state analysis

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LMSK: a robust higher-order gradient-based adaptive algorithm

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IET SIGNAL PROCESSING 2019年第5期13卷 506-515页

作者： Eghbal, Meysam Kazemi Alipoor, Ghasem Hamedan Univ Technol Elect Engn Dept Shahid Fahmideh Blvd Hamadan Iran

It has been shown that, in intensely noisy environments, adaptive algorithms based on higher-order statistics can enjoy better performance, as compared with the well known second-order least-mean-square (LMS) algorithm. By contrast, this advantage diminishes for low signal-to-noise ratio (SNR) levels, where the LMS algorithm outperforms. One remedy is to employ the LMS algorithm in conjunction with a higher-order adaptation algorithm, in a mixed mode. least-mean kurtosis (LMK) is a higher-order algorithm that has been shown to be advantageous to use if the noise distribution is Gaussian or super-Gaussian. In this study, the authors propose the LMS/kurtosis algorithm, a stochastic gradient-based adaptive algorithm that is a combination of the LMS and the LMK algorithms. Simulation results demonstrate the privilege of the proposed algorithm, in comparison with its counterparts, for a wide range of noise distributions and SNR levels. This improvement is achieved in spite of a negligible increase in computational complexity. An analytical model is also derived for the mean weight as well as the weight-error covariance matrix, from which the mean-square-error behaviour of the algorithm can be predicted. Simulation results show the high accuracy of the derived model in different conditions.

关键词： least mean squares methods higher order statistics gradient methods stochastic processes adaptive filters computational complexity filtering theory covariance matrices mean-square-error behaviour higher-order gradient-based adaptive algorithm intensely noisy environments adaptive algorithms higher-order statistics least-mean-square algorithm low signal-to-noise ratio LMS algorithm outperforms higher-order adaptation algorithm least-mean kurtosis higher-order algorithm noise distribution LMS kurtosis algorithm stochastic gradient-based adaptive algorithm LMK algorithms

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A variable step-size diffusion LMS algorithm with a quotient form

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2020年第1期2020卷 12-12页

作者： Bin Saeed, Muhammad Omer Zerguine, Azzedine Natl Univ Sci & Technol Dept Comp Engn Coll E&ME Islamabad Pakistan King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

A new variable step-size strategy for the least mean square (LMS) algorithm is presented for distributed estimation in adaptive networks using the diffusion scheme. This approach utilizes the ratio of filtered and windowed versions of the squared instantaneous error for iteratively updating the step-size. The result is that the dependence of the update on the power of the error is reduced. The performance of the algorithm improves even though it is at the cost of added computational complexity. However, the increase in computational complexity can be minimized by careful manipulation of the update equation, resulting in an excellent performance-complexity trade-off. Complete theoretical analysis is presented for the proposed algorithm including stability, transient and steady-state analyses. Extensive experimental analysis is then done to show the performance of the proposed algorithm under various scenarios.

关键词： Variable step-size least-mean-square algorithm Adaptive networks mean-square analysis Steady-state analysis

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Performance of fractional delay estimation in joint estimation algorithm dedicated to digital Tx leakage compensation in FDD transceivers

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2017年第1期2017卷 1页

作者： Gerzaguet, Robin Ros, Laurent Belveze, Fabrice Brossier, Jean-Marc Univ Rennes 1 IRISA F-38000 Grenoble France GIPSA Lab Image & Signal Dept F-38400 St Martin Dheres France ST Microelect F-38000 Grenoble France

This paper deals with the performance of the fractional delay estimator in the joint complex amplitude/delay estimation algorithm dedicated to digital Tx leakage compensation in FDD transceivers. Such transceivers suffer from transmitter-receiver signal leakage. Combined with non linearity of components in the receiver path, the baseband received signal is impaired by a baseband polluting signal. This baseband polluting term depends on the equivalent Tx leakage channel which models leakages of the receiver path. In (Gerzaguet et al., Digit. Signal Proc 51:35-46, 2016), we have proposed a Tx leakage interference cancellation algorithm based on joint estimation of the complex gain and the fractional delay of the equivalent Tx leakage channel and we have derived the asymptotic performance of the complex gain estimator, that showed the necessity of the fractional delay estimation. In this paper, we propose a comprehensive study of the fractional delay estimation algorithm and its analytic performance. The study is based on the analysis of the S-curve and loop noise variance of the timing error detector, from which an approximation of the asymptotic performance of the joint estimation algorithm is derived.

关键词： Tx leakage FDD transceiver Digital compensation least-mean-square algorithm Joint estimation S-curve

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Real-time tracking control of electro-hydraulic force servo systems using offline feedback control and adaptive control

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ISA transactions 2017年 67卷 356-370页

作者： Gang Shen Zhencai Zhu Jinsong Zhao Weidong Zhu Yu Tang Xiang Li School of Mechatronic Engineering China University of Mining and Technology Xuzhou 221116 China Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment China University of Mining and Technology Xuzhou 221116 China. Electronic address: shenganghit@***. Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment China University of Mining and Technology Xuzhou 221116 China. Electronic address: zhuzhencai@vip.***. Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control Yanshan University Qinhuangdao 066004 China. Electronic address: jinsongzhao@***. Department of Mechanical Engineering University of Maryland Baltimore County Baltimore MD 21250 USA. Electronic address: wzhu@umbc.edu. Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment China University of Mining and Technology Xuzhou 221116 China. Electronic address: tangyumail@***. Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment China University of Mining and Technology Xuzhou 221116 China. Electronic address: lixiang_nn@***.

This paper focuses on an application of an electro-hydraulic force tracking controller combined with an offline designed feedback controller (ODFC) and an online adaptive compensator in order to improve force tracking performance of an electro-hydraulic force servo system (EHFS). A proportional-integral controller has been employed and a parameter-based force closed-loop transfer function of the EHFS is identified by a continuous system identification algorithm. By taking the identified system model as a nominal plant model, an H offline design method is employed to establish an optimized feedback controller with consideration of the performance, control efforts, and robustness of the EHFS. In order to overcome the disadvantage of the offline designed controller and cope with the varying dynamics of the EHFS, an online adaptive compensator with a normalized least-mean-square algorithm is cascaded to the force closed-loop system of the EHFS compensated by the ODFC. Some comparative experiments are carried out on a real-time EHFS using an xPC rapid prototype technology, and the proposed controller yields a better force tracking performance improvement.

关键词： Adaptive control Electro-hydraulic force servo system H(∞) control least-mean-square algorithm System identification

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