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2-D adaptive state-space filters based on the Fornasini-Marchesini second model

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 1997年第8期44卷 667-670页

作者： Hinamoto, T Doi, A Muneyasu, M Faculty of Engineering Hiroshima University Higashihiroshima Japan

Based on the Fornasini-Marchesini second model, a technique is developed for implementing two-dimensional (2-D) adaptive state-space Biters. First, the relationship between the coefficient sensitivities and the intermediate transfer functions is investigated for the Fornasini-Marehesini second model, A least mean square (lms) adaptive algorithm is then presented by using new systems that generate the gradient signals, Finally, a 2-D adaptive fine enhancer is constructed by using the 2-D adaptive state-space filter to illustrate the utility of the proposed technique.

关键词： adaptive filter Fornasini-Marchesini's second model lms algorithm 2-D system

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Performance of Rlms algorithm in Adaptive Array Beam Forming

Performance of RLMS Algorithm in Adaptive Array Beam Forming

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The 11th IEEE International Conference on Communications Systems(第11届电子和电气工程师协会国际通信系统会议)(IEEE ICCS 2008)

作者： Jalal Abdulsayed Srar Kah-Seng Chung Department of Electrical and Computer Engineering Curtin University of TechnologyPerthWA

This paper examines the performance of an adaptive linear array employing the new Rlms algorithm, which consists of a recursive least square (RLS) section followed by a least mean square (lms) section. The performance measures used are output and input signal-to-interference plus noise ratios (SINR), side lobe level (SLL), and SINRo as a function of the direction of arrival of the interfering signal. Computer simulation results show that the performance of Rlms is superior to either the RLS or lms based on these measures, particularly when operating with low input SINR.

关键词： RLS algorithm lms algorithm Rlms algorithm,adaptive antenna array beam forming.

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Studies on the convergence speed of over-sampled subband adaptive digital filters

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2000年第8期E83A卷 1531-1538页

作者： Ohno, S Sakai, H Shimane Univ Dept Math & Comp Sci Matsue Shimane 6908504 Japan Kyoto Inst Technol Grad Sch Informat Dept Syst Sci Kyoto 6068501 Japan

To evaluate or compare the convergence speed of adaptive digital filters (ADF) with least mean squared (lms) algorithm, the condition numbers of correlation matrices of tap-input vectors are often used. In this paper, however, the comparison of the conventional fullband ADF and the subband ADF based on their condition numbers is shown to be invalid. In some cases, the over-sampled subband ADF converges faster than the fullband ADF, although the former has larger condition numbers. To explain the above phenomenon, an expression for the convergence behavior of the subband ADF and simulation results are provided.

关键词： lms algorithm subband ADF convergence speed

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Milling stability prediction and adaptive chatter suppression considering helix angle and bending

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2018年第9-12期95卷 3665-3677页

作者： Wang, Chenxi Zhang, Xingwu Cao, Hongrui Chen, Xuefeng Xiang, Jiawei Xi An Jiao Tong Univ Sch Mech Engn State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Wenzhou Univ Key Lab Laser Precis Proc & Detect Zhejiang Prov Key Lab Laser Proc Robot Wenzhou 325035 Peoples R China

In milling process, chatter is one of the most unfavorable factors, which will reduce surface quality, limit tool life, accelerate tool wear, and decrease machining efficiency. To solve this problem, a great deal of research has been done in milling dynamic modeling and chatter suppression. In this paper, a new milling force calculation method considering helix angle and bending is presented, in which the instantaneous cutting area is calculated in an improved way. The milling dynamic equations are established based on the proposed model, and the stability limit is obtained with semi discretization method (SDM). Results show that tool bending and helix play important roles in stability lobe diagram (SLD). Subsequently, the stability prediction is verified in the milling experiment. Stability analysis can just provide the guidance for selection of milling parameters. In order to get higher efficiency and larger stable region, the time-domain least mean square (lms) adaptive algorithm is constructed and implemented for chatter suppression in this article. For the sake of applying the method to experiments, the smart toolholder equipped with piezoelectric stack actuators is designed and mounted to a three-axis milling machine. The experimental results show that this method can suppress chatter effectively.

关键词： Helical end mills Tool bending Dynamic modeling Chatter suppression lms algorithm

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Bayesian step least mean squares algorithm for Gaussian signals

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IET SIGNAL PROCESSING 2020年第8期14卷 506-512页

作者： Lopes, Paulo A. C. Univ Lisbon Inst Super Tecn Inst Enn Sistemas & Comp Invest & Desenvolvimento INESC ID IST UL Rua Alves Redol 9 P-1000029 Lisbon Portugal

Selecting the step of the least mean squares (lms) algorithm is an old problem. This study uses a new approach to address this problem resulting in a new algorithm with excellent system identification performance. The lms algorithm, with time-varying step, size can be shown to be equivalent to the Kalman filter in some conditions. This is as long as the state noise of the Kalman filter and the step size of the lms algorithm are chosen carefully. The Kalman filter is the optimum linear estimator (Bayesian) given the state and the measurement noise covariance matrices, but these matrices are not always known. This work considers the case where these matrices are not known, in the special cases that the Kalman filter reduces to the lms. This results in an algorithm to select the step-size of the lms algorithm with few priors. The optimum step size can be calculated using estimates of the probability density function (PDF) of the coefficient estimation error variance (q(w)) and measurement noise variance (q(v)). The PDFs can be estimated from the data using Bayes' rule and assuming Gaussian reference and measurement noise signals. The resulting algorithm to determineq(w)andq(v)is a second small Kalman filter, and the outputs of this filter (means and covariances) are used to determine the expected value of the step.

关键词： covariance matrices filtering theory Bayes methods least mean squares methods Kalman filters Gaussian processes system identification performance lms algorithm time-varying step Kalman filter state noise optimum linear estimator measurement noise covariance matrices optimum step size Bayesian step least mean squares algorithm Gaussian signals probability density function coefficient estimation error variance

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Orthogonal adaptation for active noise control

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JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2006年第1期120卷 204-210页

作者： Yuan, Jing Hong Kong Polytech Univ Dept Mech Engn Kowloon Hong Kong Peoples R China

Many active noise control (ANC) systems apply the filtered-x least mean squares (FXlms) algorithm for controller adaptation. The accuracy of path models is an important issue in these systems. Since parameter drifting in a noise field may cause model error between the secondary path and its prestored model in an ANC system, some ANC systems employ two adaptive processes for path modeling and controller adaptation respectively. In this paper, a new ANC system is proposed with adaptive path modeling and nonadaptive controller design. The proposed ANC system is noninvasive without persistent excitations. It avoids the slow convergence and inevitable estimation errors in controller adaptation. A rigorous analysis is presented to prove that the new ANC system will converge to an optimal one in the minimum H-2 norm sense. Experimental results are presented to verify the performance of the proposed ANC system. (c) 2006 Acoustical Society of America.

关键词： SYSTEM-IDENTIFICATION lms algorithm

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Learning strategies for adaptive equalizers using the constant modulus error criterion

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 1998年第2期12卷 97-116页

作者： Soni, RA Schirtzinger, TA Jenkins, WK Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA

In communication systems, data are often corrupted during transmission due to imperfect communication channels. Since channel characteristics may be time varying or unknown prior to data transmission, adaptive equalizers are typically incorporated into the receiver to reduce the ill effects of the imperfect channel. Conventional equalizers utilize a training signal to achieve the proper correction. Blind equalization schemes do not require the use of a training signal, but instead attempt restoration based upon some known property of the transmitted signal. The constant modulus algorithm (CMA) is a blind equalization technique that may be used to equalize certain communication signals (e.g. BPSK, QPSK, and FM). This paper investigates three new techniques for adaptive equalization based on the constant modulus error criterion: (i) the fast quasi-Newton CMA, (ii) the conjugate gradient quasi-Newton CMA, (iii) the conjugate gradient CMA. These three methods are formulated analytically and evaluated experimentally on several typical communication channel models. (C) 1998 John Wiley & Sons, Ltd.

关键词： adaptive channel equalization blind equalization conjugate gradient algorithm constant modulus algorithm fast quasi-Newton CMA algorithm lms algorithm

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Total least mean squares algorithm

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1998年第8期46卷 2122-2130页

作者： Feng, DZ Bao, Z Jiao, LC Xidian Univ Elect Engn Res Inst Xian 710071 Peoples R China

Widrow proposed the least mean squares (lms) algorithm, which has been extensively applied in adaptive signal processing and adaptive control, The lms algorithm is based on the minimum mean squares error, On the basis of the total least mean squares error or the minimum Raleigh quotient, we propose the total least mean squares (Tlms) algorithm. The paper gives the statistical analysis for this algorithm, studies the global asymptotic convergence of this algorithm by an equivalent energy function, and evaluates the performances of this algorithm via computer simulations.

关键词： Hebb learn rule lms algorithm stability statistical analysis system identification unsupervised learning

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Analogue discrete-time basic structures for adaptive IIR filters

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IEE PROCEEDINGS-CIRCUITS DEVICES AND SYSTEMS 2000年第4期147卷 250-256页

作者： Ndjountche, T Unbehauen, R Univ Erlangen Nurnberg Lehrstuhl Allgemeine & Theoret Elektrotech D-91058 Erlangen Germany

An analogue discrete-time implementation of an adaptive infinite-impulse-response (IIR) filter is presented. The filter structure uses the IIR modelling capability of first- and second-order filter sections and the adaptation process is a variable step-size version of the least-mean-squared (lms) algorithm. The gradient signals were chosen to allow the use of the whole dynamic range of the four-quadrant analogue multiplier. With respect to implementation problems, the effects of nonidealities on the building elements an investigated and reduced using appropriate circuit techniques. The functional behaviour of the proposed structure is demonstrated for a low-complexity third-order adaptive filter with a parallel architecture.

关键词： IIR modelling capability least-mean-squared algorithm network synthesis four-quadrant analogue multiplier parallel architecture least mean squares methods analogue discrete-time implementation lms algorithm infinite-impulse-response filter SC integrator adaptive filters programmable filters nonidealities low-complexity third-order adaptive filter SC filter section IIR filters adaptive IIR filters discrete time filters variable step-size version analogue multipliers switched capacitor filters

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Background noise suppression for signal enhancement by novelty filtering

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2000年第1期36卷 102-113页

作者： Ko, H Arozullah, M Korea Univ Sch Elect Engn Sungbuk Ku Seoul 136701 South Korea Catholic Univ Amer Dept Elect Engn Washington DC 20065 USA

The enhancement of weak signals in the presence of background and channel noise is necessary to design a robust automatic signal detection and recognition system. The autoassociative property of neural networks can be used to map the identifying characteristics of input source waveforms or their spectra. This paper is directed at the exploitation of such neural network properties for novelty filtering that improves the detection probability of weak signals hy learning and subsequent subtraction of noise background from the input waveform. A neural-network-based preprocessor that learns to selectively filter out the background noise without significantly affecting the signal will be highly useful in solving practical signal enhancement problems. An analytical basis is established for the operation of neural-network-based novelty filters that enhance the signal detectability in the presence of noise background and channel noise.

关键词： backpropagation feedforward neural nets filtering theory matched filters signal detection tracking filters Haar wavelet lms algorithm autoassociative property automatic signal recognition system background noise suppression channel noise channel noise matched filters feedforward neural nets back propagation BACKGROUND NOISE tracking filters Least mean square algorithm signal acquisition filtering theory signal enhancement

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