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中国中西医结合肾病杂志 2025年第3期26卷 223-225页

作者：张珊傅雪茜杨鹏鹏潘岳鑫李永伟孙响波青岛市市北区延安路街道社区卫生服务中心(2021级青岛市中医医院规培学员) 青岛266000 青岛大学附属青岛市海慈医院(青岛市中医医院) 青岛266000

目的:观察穴位埋线治疗尿毒症维持性血液透析(MHD)患者不宁腿综合征(rls)的临床疗效与安全性,并阐述其治疗机理。方法:80例MHD患者采用随机数字表法分为对照组与观察组,两组均取双侧足三里、丰隆、委阳、承山穴,在常规治疗的基础上,对... 详细信息

目的:观察穴位埋线治疗尿毒症维持性血液透析(MHD)患者不宁腿综合征(rls)的临床疗效与安全性,并阐述其治疗机理。方法:80例MHD患者采用随机数字表法分为对照组与观察组,两组均取双侧足三里、丰隆、委阳、承山穴,在常规治疗的基础上,对照组予普通针刺治疗,每次留针30 min,每周治疗3次,共治疗8周。观察组予穴位埋线治疗,每周治疗1次,共治疗8周。比较两组治疗前后不宁腿严重程度量表(Irls)、匹斯堡睡眠质量指数(PSQI)的数值评分;观察血清甲状旁腺激素(iPTH)、β_(2)微球蛋白(β_(2)-MG)、血红蛋白(Hb)、血清铁(Fe 3+)、血钙(Ca^(2+))、血磷(P^(3-))等实验室与疗效评价及安全性指标的变化。结果:观察组总有效率为92.68%,对照组总有效率为79.49%。治疗后两组患者Irls、PSQI评分均较治疗前下降,观察组评分的改善状况优于对照组(P<0.01)。结论:穴位埋线治疗MHD患者rls疗效显著,治疗机理与调节局部神经传导、改善局部血液循环,抑制局部炎症反应相关。

关键词：穴位埋线 MHD rls 临床疗效作用机理

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rls-Based On-Line Sparse Nonnegative Matrix Factorization Method for Acoustic Signal Processing Systems

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2013年第5期E96A卷 980-985页

作者： Lee, Seokjin LG Elect Inc Seoul 151742 South Korea

Recursive least squares-based online nonnegative matrix factorization (rls-ONMF), an NMF algorithm based on the rls method, was developed to solve the NMF problem online. However, this method suffers from a partial-data problem. In this study, the partial-data problem is resolved by developing an improved online NMF algorithm using rls and a sparsity constraint. The proposed method, rls-based online sparse NMF (rls-OSNMF), consists of two steps;an estimation step that optimizes the Euclidean NMF cost function, and a shaping step that satisfies the sparsity constraint. The proposed algorithm was evaluated with recorded speech and music data and with the RWC music database. The results show that the proposed algorithm performs better than conventional rls-ONMF, especially during the adaptation process.

关键词： NMF ALS-NMF rls on-line NMF sparse NMF

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Blind Equalization Based on rls Algorithm Using Adaptive Forgetting Factor for Underwater Acoustic Channel

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China Ocean Engineering 2014年第3期28卷 401-408页

作者：肖瑛殷福亮 College of Information and Communication Engineering Dalian Nationality University Faculty of Electronic Information and Electrical Engineering Dalian University of Technology

Blind equalization based on adaptive forgetting factor, recursive least squares （rls） with constant modulus algorithm （CMA）, is investigated. The cost function of CMA is simplified to meet the second norm form to ensure the stability of rls-CMA, and thus an improved rls-CMA （rls-SCMA） is established. To further improve its performance, a new adaptive forgetting factor rls-SCMA （Arls-SCMA） is proposed. In Arls-SCMA, the forgetting factor varies with the output error of the blind equalizer during the iterative process, which leads to a faster convergence rate and a smaller steady-state error. The simulation results prove the effectiveness under the condition of the underwater acoustic channel.

关键词： blind equalization rls CMA underwater acoustic channel second norm form

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rls-based deadbeat predictive current control for dual three-phase segmented powered linear motors

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JOURNAL OF POWER ELECTRONICS 2024年第6期24卷 922-933页

作者： Zhou, Shijiong Li, Yaohua Shi, Liming Guo, Keyu Fan, Manyi Liu, Jinhai Chinese Acad Sci Inst Elect Engn Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China China North Vehicle Res Inst Beijing Peoples R China

Deadbeat predictive current control (DPCC) is an effective model-based motor control method. However, due to the unbalanced inductance and parameter variations of the segmented powered linear motor stator, the conventional model of linear motors is not accurate, which ultimately affect the performance of the control. This paper proposes a novel DPCC based on the recursive least squares (rls) method to identify the parameters of the dual three-phase segmented powered linear motor (SP-LM) model. First, the influence of unbalanced inductance caused by the segmented motor stator and parameter variations of the conventional DPCC are analyzed. Second, a discrete rls model of the dual three-phase SP-LM is established, which is a common model for both linear induction motors (LIMs) and linear synchronous motors (LSMs). Finally, the model parameters are identified by the rls method and the deadbeat principle is used to predict the current. The proposed method effectively eliminates the influence of unbalanced inductance and the parameter variation, improves the current control performance and reduces the thrust fluctuation. Experiments based on hardware-in-the-loop verify the proposed method.

关键词： DPCC rls Parameters variation Unbalance inductance Common model

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Gradient-based variable forgetting factor rls algorithm in time-varying environments

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2005年第8期53卷 3141-3150页

作者： Leung, SH So, CF City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

In this paper, a new control mechanism for the variable forgetting factor (VFF) of the recursive least square (rls) adaptive algorithm is presented. The control algorithm is basically a gradient-based method of which the gradient is derived from an improved mean square error analysis of rls. The new mean square error analysis exploits the correlation of the inverse of the correlation matrix with itself that yields improved theoretical results, especially in the transient and steady-state mean square error. It is shown that the theoretical analysis is close to simulation results for different forgetting factors and different model orders. The analysis yields a dynamic equation of mean square error that can be used to derive a dynamic equation of the gradient of mean square error to control the forgetting factor. The dynamic equation can produce a positive gradient when the error is large and a negative gradient when the error is in the steady state. Compared with other variable forgetting factor algorithms, the new control algorithm gives fast tracking and small mean square model error for different signal-to-noise ratios (SNRs).

关键词： mean square error analysis rls time-varying variable forgetting factor

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Raman Laser Spectrometer (rls) calibration target design to allow onboard combined science between the rls and MicrOmega instruments on the ExoMars rover

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JOURNAL OF RAMAN SPECTROSCOPY 2020年第9期51卷 1718-1730页

作者： Lopez-Reyes, Guillermo Pilorget, Cedric Moral, Andoni G. Antonio Manrique, Jose Sanz, Aurelio Berrocal, Alicia Veneranda, Marco Rull, Fernando Medina, Jesus Hamm, Vincent Bibring, Jean-Pierre Antonio Rodriguez, Jose Perez Canora, Carlos Mateo-Marti, Eva Prieto-Ballesteros, Olga Lalla, Emmanuel Vago, Jorge L. Univ Valladolid Unidad Asociada UVa CSIC CAB Valladolid Spain Univ Paris Sud Inst Astrophys Spatiale Orsay France INTA Dept Programas Espaciales Torrejon De Ardoz Spain Ingn Sistemas Def Espana SA ISDEFE Madrid Spain Ctr Astrobiol CAB Dept Planetol & Habitabilidad Torrejon De Ardoz Spain York Univ Ctr Res Earth & Space Sci Toronto ON Canada European Space Agcy European Space Res & Technol Ctr ESTEC Noordwijk Netherlands

The ExoMars rover, scheduled to be launched in 2020, will be equipped with a novel and diverse payload. It will also include a drill to collect subsurface samples (from 0- to 2-m depth) and deliver them to the rover analytical laboratory, where it will be possible to perform combined science between instruments. For the first time, the exact same sample target areas will be investigated using complementary analytical methods-infrared spectrometry, Raman spectrometry, and laser desorption mass spectrometry-to establish mineralogical and organic chemistry composition. Fundamental for implementing this cooperative science strategy is the Raman Laser Spectrometer (rls) calibration target (CT). The rls CT features a polyethylene terephthalate disk used for rls calibration and verification of the instrument during the mission. In addition, special patterns have been recorded on the rls CT disk that the other instruments can detect and employ to determine their relative position. In this manner, the rls CT ensures the spatial correlation between the three analytical laboratory instruments: MicrOmega, rls, and MOMA. The rls CT has been subjected to a series of tests to qualify it for space utilization and to characterize its behavior during the mission. The results from the joint work performed by the rls and MicrOmega instrument teams confirm the feasibility of the "combined science" approach envisioned for ExoMars rover operations, whose science return is optimized when complementing the rls and MicrOmega joint analysis with the autonomous rls operation.

关键词： calibration target rls MicrOmega ExoMars combined science

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The KaGE rls algorithm

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2003年第12期51卷 3094-3104页

作者： Skidmore, ID Proudler, IK QinetiQ Ltd Malvern WR14 3PS Worcs England

A new fast recursive least squares (rls) algorithm is introduced. By making use of rls interpolation as well as prediction, the algorithm generates the transversal filter weights without suffering the poor numerical attributes of the FTF algorithm. The Kalman gain vector is generated at each time step in terms of interpolation residuals. The interpolation residuals are calculated in an order recursive manner. For an Nth-order problem, the procedure requires O(N log(2) N) operations per iteration. This is achieved via a divide-and-conquer approach. Computer simulations suggest that the new algorithm is numerically robust, running successfully for many millions of iterations.

关键词： adaptive filtering algorithms fast algorithms fast rls rls stable

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Group sparse rls algorithms

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2014年第12期28卷 1398-1412页

作者： Eksioglu, Ender M. Istanbul Tech Univ Elect & Commun Engn Dept TR-34469 Istanbul Turkey

Group sparsity is one of the important signal priors for regularization of inverse problems. Sparsity with group structure is encountered in numerous applications. However, despite the abundance of sparsity-based adaptive algorithms, attempts at group sparse adaptive methods are very scarce. In this paper, we introduce novel recursive least squares (rls) adaptive algorithms regularized via penalty functions, which promote group sparsity. We present a new analytic approximation for (p,0) norm to utilize it as a group sparse regularizer. Simulation results confirm the improved performance of the new group sparse algorithms over regular and sparse rls algorithms when group sparse structure is present. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： adaptive filter rls sparsity group sparsity block structure mixed norm

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rls Algorithm With Convex Regularization

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IEEE SIGNAL PROCESSING LETTERS 2011年第8期18卷 470-473页

作者： Eksioglu, Ender M. Tanc, A. Korhan Istanbul Tech Univ Dept Elect & Commun Engn TR-80626 Istanbul Turkey

In this letter, the rls adaptive algorithm is considered in the system identification setting. The rls algorithm is regularized using a general convex function of the system impulse response estimate. The normal equations corresponding to the convex regularized cost function are derived, and a recursive algorithm for the update of the tap estimates is established. We also introduce a closed-form expression for selecting the regularization parameter. With this selection of the regularization parameter, we show that the convex regularized rls algorithm performs as well as, and possibly better than, the regular rls when there is a constraint on the value of the convex function evaluated at the true weight vector. Simulations demonstrate the superiority of the convex regularized rls with automatic parameter selection over regular rls for the sparse system identification setting.

关键词： Adaptive filter convex regularization l1 norm l0 norm rls sparsity

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Excision of Ocular Artifacts from EEG Using NVFF-rls Adaptive Algorithm

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2017年第1期36卷 404-419页

作者： Garg, Harish Kumar Kohli, Amit Kumar Thapar Univ Dept Elect & Commun Engn Patiala 147004 Punjab India

This paper presents a technique for the removal of ocular artifacts from electro-encephalogram (EEG) by using adaptive filtering. The major concern is electro-oculogram (EOG) signal present in a recorded EEG signal, which appears due to abrupt eye movements. In the presented method, we use separately recorded horizontal EOG (HEOG) and vertical EOG (VEOG) signals as two reference inputs, which are processed using finite impulse response (FIR) filters. The linear filter coefficients are adaptively updated using a numerical variable forgetting factor (NVFF) recursive least squares (rls) algorithm, which tracks nonstationary EOG signals. Subsequently, the processed HEOG and VEOG signals are subtracted from recorded EEG signal to obtain an artifact-free EEG signal. Simulation is conducted using synthetic EEG signal corrupted by noise, synthetic HEOG and VEOG signals. The real-time recorded EEG signal (corrupted by EOG and noise) is also refined using the separately recorded reference EOG signals and FIR filtering technique. For synthetic and real-time signals, the simulation results are presented to demonstrate that linear NVFF-rls algorithm-based artifact and noise excision technique outperforms conventional fixed forgetting factor rls, fixed step-size NLMS and generalized variable step-size NLMS algorithms, in terms of the reduction in mean-squared error, under low as well as high signal-to-noise ratio conditions.

关键词： EEG EOG Artifacts rls Normalized least mean square (NLMS) Numeric variable forgetting factor (NVFF) Fixed forgetting factor (FFF) Fixed step-size (FSS) Generalized variable step-size (GVSS) algorithm

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