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CONVERGENCE ANALYSIS OF CONSTRAINED JOINT ADAPTATION IN RECORDING CHANNELS

IFAC Proceedings Volumes 2005年第1期38卷 119-124页

作者： Lim Sze Chieh George Mathew

Although partial response (PR) equalization employing the linearly constrained least-mean-square (LClms) algorithm is widely used in recording channels, there is no literature on its convergence analysis. Existing analyses of the lms algorithm assume that the input signals are jointly Gaussian, which is an invalid assumption for PR equalization with binary input. In this paper, we present a convergence analysis of the LClms algorithm, without the Gaussian assumption. An approximate expression is derived for the misadjustment. It is shown that the step-size range required to guarantee stability is larger for binary data compared to Gaussian data.

关键词： Adaptation adaptive equalization constraints lms algorithm mean-square error partial response channels recording channels PR target

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Design of 2-D separable-denominator adaptive statespace digital filters using stable filter structures

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IFAC Proceedings Volumes 2004年第12期37卷 427-432页

作者： Takao Hinamoto Yoshiyuki Kawai Mitsuji Muneyasu Akimitsu Doi Hiroshima University Higashi-Hiroshima 739-8527 Japan Kansai University Suita 564-8680 Japan Hiroshima Institute of Technology Hiroshima 731-5193 Japan

A new technique for the design of two-dimensional (2-D) separable-denominator adaptive state-space digital filters is developed using stable filter structures. First, coefficient sensitivites are related to intermediate transfer functions in order to generate gradient signals. Next, the lms algorithm is applied to construct adaptive state-space digital filters with new systems to generate gradient signals. Finally, a numerical example is given to illustrate the validity of the proposed technique. Comparison between the proposed and conventional adaptive filters is also presented in the example.

关键词： 2-D adaptive filters 2-D separable-denominator digital filter Roessor’s local state-space model lms algorithm intermidiate functions

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Cooperative Neural Modules for the Visual Servoing of a Robot Arm

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IFAC Proceedings Volumes 1997年第6期30卷 1041-1047页

作者： J.P. Urban J.L. Buessler H. Kihl J. Gresser Université de Haute-Alsace Laboratoire TROP 4 r. des Frères Lumière - 68093 Mulhouse Cedex - France

This paper presents neural network based self-adapting modules for the visual servoing of a robot manipulator. Arm movements are controlled using visual features. The neural modules guarantee the fast learning and memorization of the nonlinear mapping functions. The arm can be controlled either with a single module, or better with several parallel and cooperative modules using each a subset of the visual input features. The approach is applied to the control of a 3 d.o.f. Planar arm. The use of two interconnected modules reduces the size of the neural networks involved in the process and facilitates the synchronization of the reaching and grasping preparation tasks.

关键词： Robot vision Neural networks Adaptive correlation lms algorithm Jacobian matrices Closed-loop control Arm movements Memory units

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Adaptive Interference Cancellation of ECG Signals

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SENSORS 2017年第5期17卷 942-942页

作者： Ren, Aifeng Du, Zhenxing Li, Juan Hu, Fangming Yang, Xiaodong Abbas, Haider Xidian Univ Sch Elect Engn Xian 710071 Peoples R China King Saud Univ Riyadh 11653 Saudi Arabia NUST Islamabad 44000 Pakistan Florida Inst Technol Dept Comp Sci Melbourne FL 32901 USA

As an important biological signal, electrocardiogram (ECG) signals provide a valuable basis for the clinical diagnosis and treatment of several diseases. However, its reference significance is based on the effective acquisition and correct recognition of ECG signals. In fact, this mV-level weak signal can be easily affected by various interferences caused by the power of magnetic field, patient respiratory motion or contraction, and so on from the sampling terminal to the receiving and display end. The overlapping interference affects the quality of ECG waveform, leading to the false detection and recognition of wave groups, and thus causing misdiagnosis or faulty treatment. Therefore, the elimination of the interference of the ECG signal and the subsequent wave group identification technology has been a hot research topic, and their study has important significance. Based on the above, this paper introduces two improved adaptive algorithms based on the classical least mean square (lms) algorithm by introducing symbolic functions and block-processing concepts.

关键词： ECG signal interference cancellation lms algorithm

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A LUT-based scheme for LNA linearization in direct RF sampling receivers

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PHYSICAL COMMUNICATION 2022年 50卷

作者： Ngoc-Anh Vu Hai-Nam Le Thi-Hong-Tham Tran Quang-Kien Trinh Le Quy Don Tech Univ Fac Radioelect Hanoi Vietnam

Direct RF sampling receiver - a fully digital receiver architecture - undoubtedly becomes a favored choice for HF/VHF as this approach inherently bypasses the legacy nonlinearities caused by analog components. In DRF-RF and wideband multichannel in general, LNA is still an indispensable component to ensure the receiver's sensitivity. However, with the presence of multiple channels, the total RF power often surpasses the linear threshold that LNA and the amplified signal become severely distorted. This paper proposed a method for mitigating the LNA distortion using the look-up table (LUT) approach. Specifically, our receiver is designed with two modes of operation. In training mode, a built-in signal circuit generates a training signal for extracting the LNA characteristic and eventually reconstructs the inverse LNA nonlinear model in the form of a LUT memory. During the receiving mode, a linearization circuit reverses the distortion impact by matching the RF power level with the inverse nonlinear model pre-stored in the LUT. The effectiveness of the proposed distortion compensation method first is evaluated by a MATLAB simulation with a multi-channel DRF-RF model. The simulation results show that the proposed approach significantly improved the SNDR for the channel of interest. Furthermore, the model has been practically verified, where the actual distorted signals are sampled from a commercial LNA (ZFL-500LN+) by a customized FPGA board. Results from measurements show an improvement of similar to 7 dB for SNDR and 27% for EVM in a strong distortion scenario of QPSK modulation signal. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Direct-sampling RF receiver DCR LNA distortion Digital receiver lms algorithm Distortion inversion Multichannel receiver Software-defined radio HF/VHF transceiver

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Improved variable step-size least mean square algorithm based on Sigmoid function

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Procedia Computer Science 2022年 199卷 1466-1473页

作者： Wei Yu Qing Cheng Sihai Guan College of Electronic and Information Southwest Minzu University Chengdu 610225 China Sichuan Vocational College of Finance and Economics Chengdu 610101 China Key Laboratory of Electronic and Information Engineering State Ethnic Affairs Commission Chengdu 610225 China

One of the research focuses on the least mean square (lms) algorithm is how to design the variable step-size rule to make the lms algorithm converge ratio and steady-state error. The step-size is relatively large in its early stages; that is, the algorithm at this stage converges quickly; when the algorithm tends to a steady-state, the step-size is relatively small, that is. It is at this stage that the steady-state estimation error is relatively tiny. Therefore, looking at it as a whole, the algorithm can converge quickly with lower steady-state errors. Therefore, this paper designs a new step-size rule based on the Sigmoid function and theoretically analyzes the convergence characteristics and steady-state performance. Experiments and simulation comparisons are carried out under the conditions of comparison. The theoretical analysis combined with experimental simulation verification: even if the linear system has a sudden change, this algorithm still has a faster convergence ratio and tracking speed and can obtain minor steady-state errors and steady-state offsets.

关键词： Channel estimation lms algorithm Sigmoid function steady-state error convergence ratio

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''Individually normalized'' least mean square algorithm suitable for fixed-point processing and its application to active noise control

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ELECTRONICS AND COMMUNICATIONS IN JAPAN PART III-FUNDAMENTAL ELECTRONIC SCIENCE 1996年第4期79卷 62-73页

作者： Fujii, K Ohga, J Member Fujitsu Laboratories Ltd. Kawasaki Japan 211

By the fixed-point processing, a cheap and fast processor can be obtained which is an effective approach in reducing the production cost. In the adaptive filter, where the coefficient correction which is calculated in each sampling period to be added to the filter coefficient, the adjustment of the filter coefficient is made impossible if the coefficient correction is less than the minimum value that can be represented by the fixed-point representation. In the normalized least mean square (Nlms) algorithm, the normalization using the norm is applied, which increases the probability that the coefficient correction is less than the minimum value. This problem is solved by extracting the coefficient correction as the ''difference between the impulse response of the unknown signal transmission system as the object of identification and its estimation.'' In the ''individually normalized'' least mean square (lms) algorithm, this difference is derived by calculating the ratio between the product-sum of the residual response with the adaptive filter output and the square-sum of the tap output. Then, the result is normalized individually for each coefficient. This paper then presents the stability condition which serves as the design principle of the proposed method. The parallel-shifting integrating configuration is presented, which has the computational complexity less than that of the Nlms algorithm. The method is applied to the filtered-x algorithm, and the usefulness is verified.

关键词： fixed-point processing coefficient correction individual normalization lms algorithm

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Enhanced Noise Cancellation: A Variable Step Size Normalized Least Mean Square Approach

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TRAITEMENT DU SIGNAL 2024年第2期41卷 911-918页

作者： Manseur, Abdelghani Dendouga, Abdelhakim Kasdi Merbah Univ Fac New Informat Technol & Commun Dept Elect & Telecommun Ouargla 30000 Algeria Mohamed Khider Univ Fac Sci & Technol Elect Engn Dept Biskra 07000 Algeria Mohamed Khider Univ Identificat Command Control & Commun Lab LI3CUB Biskra 07000 Algeria

Noise cancellation remains a significant challenge in signal processing, particularly when addressing non-stationary and time-varying noise sources. Traditional approaches, such as the Normalized Least Mean Square (Nlms) algorithm, are often limited by the fixed step size parameter, which dictates the trade-off between convergence rate and system robustness. In this study, an innovative Variable Step Size Nlms (VSS-Nlms) algorithm is introduced, designed to dynamically adjust the step size parameter, thereby optimizing performance criteria including precision, robustness, convergence rate, and tracking ability. Employing system identification techniques within an adaptive filtering framework, this research advances the Nlms algorithm by incorporating a variable step size parameter that adapts in real-time to the noise environment. The proposed VSS-Nlms algorithm is evaluated through extensive simulations, demonstrating a significant enhancement in the balance between Mean Square Error (MSE) reduction and convergence rate over both the conventional Nlms and Recursive Least Squares (RLS) algorithms, whilst maintaining computational simplicity. In the context of adaptive filters, the VSS-NLSM algorithm represents a substantial improvement for noise cancellation applications, particularly in scenarios characterized by variable noise dynamics. The results presented herein confirm that the VSS-Nlms algorithm not only achieves a superior trade-off between accuracy/robustness and convergence rate/tracking but also sets a new benchmark for adaptive noise cancellation strategies in complex acoustic environments.

关键词： adaptive filtering noise cancellation lms algorithm Nlms algorithm VSS-Nlms algorithm variable step size convergence factor Mean Square Error (MSE)

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New way on FPGA implementation of lms adaptive filter

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Yadian Yu Shengguang/Piezoelectrics and Acoustooptics 2007年第1期29卷 87-89页

作者： Liu, Xiong-Fei Gao, Jin-Ding Qi, Hai-Bing Physics School Central South University Changsha 410083 China

Adaptive filters implemented by programming using DSP processors had a low processing speed and poor antijamming capabilities;also, adaptive filters implemented by FPGA through bottom layer HDL coding had a poor development efficiency. To solve these problems, a model of 8-taps 2FSK noise cancel adaptive filter was established and then simulated using DSP Builder. And an 8 taps adaptive filter with a processing speed of 36.63 MHz was designed out on EPF10K100EQC208-1. This processing speed was more than 7 times faster than which implemented through bottom layer VHDL coding and 25 times faster than which implemented by programming using DSP processor TMS320C54X.

关键词： 2FSK Adaptive filter DSP Builder FPGA Implementation lms algorithm

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ADAPTIVE EXPONENT SMOOTHING GRADIENT algorithm

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Transactions of Nanjing University of Aeronautics and Astronautics 1997年第1期14卷 25-31页

作者：裴炳南李传光郑州大学电子工程系北京理工大学自动控制系

A new algorithm, called the adaptive exponent smoothing gradient algorithm (AESGA), is developed from Widrow′s lms algorithm. It is based on the fact that lms algorithm has properties of time delaying and low pass filtering. This paper shows that the algorithm, on the domain of {Ω 1:α∈(0,1)}×{Ω 2:β(0,∞)} , unbiasedly and asymptotically converges to the Winner solution when the signal is a stationary Gauss stochastic process. The convergent property and the performance misadjustment are analyzed in theory. And calculation method of the algorithm is also suggested. Numerical results given by computer simulations show that the algorithm is effective.

关键词： signal processing adaptive filtering gradient algorithm lms algorithm computer simulation

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