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ANALYSIS OF LMS-NEWTON ADAPTIVE filtering algorithms WITH VARIABLE CONVERGENCE FACTOR

IEEE TRANSACTIONS ON SIGNAL PROCESSING 1995年第3期43卷 617-627页

作者： DINIZ, PSR DECAMPOS, MLR ANTONIOU, A UNIV VICTORIA DEPT ELECT & COMP ENGNVICTORIABC V8W 3P6CANADA

An analysis of two LMS-Newton adaptive filtering algorithms with variable convergence factor is presented. The relations of these algorithms with the conventional recursive least-squares algorithm are first addressed. Their performance in stationary and nonstationary environments is then studied and closed-form formulas for the excess mean-square error (MSE) are derived. The paper deals, in addition, with the effects of roundoff errors for the case of fixed-point arithmetic. Specifically, closed-form formulas far the excess MSE caused by quantization are obtained. The paper concludes with experimental results that demonstrate the validity of the analysis presented.

关键词： Algorithm design and analysis Adaptive filters filtering algorithms Convergence Signal processing algorithms Fixed-point arithmetic Least squares approximation Statistics Resonance light scattering Iterative algorithms

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Multiparameter optimization of inverse filtering algorithms

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 1996年第2期45卷 417-421页

作者： Daboczi, T Kollar, I Department of Measurement and Instrumentation Engineering Technical University of Budapest Budapest Hungary

This paper investigates inverse filtering of transient signals, The problem is ill-conditioned, which means that a small uncertainty in the measurement causes large deviations in the reconstructed signal, This amplified noise has to be suppressed at the price of bias in the estimation. The most difficult task is to find the optimal degree of noise reduction. Deconvolution algorithms are usually controlled by one or a few parameters, Several algorithms can be found in the literature to find the best setting of inverse filtering methods;however, usually methods with only one free parameter are handled. In this paper, an algorithm is proposed to optimize several parameters, on the basis of a spectral model. Multiparameter inverse filtering methods have the advantage that they can be better adapted to the measurement system, and to the noise and signal to be measured, The superiority of the proposed optimization method is demonstrated both on simulated and on experimental data.

关键词： filtering algorithms Distortion measurement Noise measurement Filters Deconvolution Optimization methods Shape measurement Senior members Measurement uncertainty Noise reduction

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A comparison of collaborative-filtering recommendation algorithms for e-commerce

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IEEE INTELLIGENT SYSTEMS 2007年第5期22卷 68-78页

作者： Huang, Zan Zeng, Daniel Chen, Hsinchen Penn State Univ Smeal Coll Business Dept Supply Chain & Informat Syst University Pk PA 16802 USA Univ Arizona Dept Management Informat Syst Tucson AZ 85721 USA

Collaborative filtering is one of the most widely adopted and successful recommendation approaches. Unlike approaches based on intrinsic consumer and product characteristics, CF characterizes consumers and products implicitly by their previous interactions. The simplest example is to recommend the most popular products to all consumers. Researchers are advancing CF technologies in such areas as algorithm design, human- computer interaction design, consumer incentive analysis, and privacy protection.

关键词： Aggregates Algorithm design and analysis Collaboration Feedback filtering algorithms Guidelines Optical wavelength conversion Prediction algorithms Privacy Protection algorithm design algorithm design and evaluation collaborative filtering collaborative-filtering recommendation algorithms e-commerce consumer incentive analysis e-commerce electronic commerce groupware human- computer interaction design information filtering information filters intrinsic consumer privacy protection product characteristics recommendation approaches recommender systems

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RECURSIVE TWO-DIMENSIONAL MEDIAN filtering algorithms FOR FAST IMAGE ROOT EXTRACTION

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS 1988年第10期35卷 1323-1326页

作者： BOLES, WW KANEFSKY, M SIMAAN, M Signal Processing and Interpretation Laboratory Department of Electrical Engineering University of Pittsburgh PA USA

The authors develop and evaluate two new two-dimensional fast recursive median filtering algorithms, for image root extraction. The first recursive algorithm, which is called the fast binary root algorithm, converges to a root of a binary image in one pass including a number of local rescans. The second is referred to as the fast multilevel root algorithm. It converges to a root of a multilevel image in two passes, the second of which includes a number of local rescans. The number of ordering positions required by the author's algorithms is shown to be significantly less than those required by the standard and regular recursive median filters.< >

关键词： filtering algorithms Filters Signal processing algorithms Image converters Convergence Pixel Statistics Magnetic resonance Noise robustness Image processing

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New adaptive IIR filtering algorithms based on the Steiglitz-McBride method

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1997年第5期45卷 1367-1371页

作者： Cousseau, JE Diniz, PSR UNIV FED RIO DE JANEIRO DEPT ELETRRIO JANEIROBRAZIL

This correspondence introduces a family of algorithms based on the Steiglitz-McBride (SM) method for adaptive IIR filtering. The main feature of the proposed algorithms is to remove the parameter bias induced by colored measurement noise. The conceptual similarities between the SM method and the equation error (EE) method are explored in order to analyze some properties of the new algorithms, The main analysis results concern the consistency of the parameter estimates and local convergence, A set of simulations in a system identification configuration is included in order to verify the improved performance of the new algorithms based on the SM method.

关键词： filtering algorithms Samarium Adaptive filters IIR filters Noise measurement Colored noise Equations Algorithm design and analysis Parameter estimation Convergence

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Robust Constrained Affine-Projection-Like Adaptive filtering algorithms Using the Modified Huber Function

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第3期70卷 1214-1218页

作者： Xu, Wenjing Zhao, Haiquan Lv, Shaohui Southwest Jiaotong Univ Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

In this brief, two robust constrained affine-projection-like M-estimate (CAPLM) adaptive filtering algorithms are proposed, which solve the problem that the traditional constrained affine projection (CAP) algorithm is not robust to impulsive interference, and realize unbiased output in some applications where the desired signal is unavailable or unnecessary. Specifically, a modified Huber function (MHF) based robust AP-like (APL) minimization problem with constraints is defined and can be transformed into two different unconstrained optimization problems by using Lipschitz continuity and two different methods, and then CAPLM-I and CAPLM-II algorithms are obtained respectively. Both CAPLM algorithms can avoid a certain amount of computational complexity caused by the inversion of the input signal matrix in classical AP algorithm, and realize the robustness to impulsive interference. In addition, the mean square stabilities of them are analyzed, and the corresponding stable step size ranges are also given. Simulation results show that the proposed CAPLM-I and CAPLM-II algorithms perform well in system identification and beamforming applications in impulsive noise environment, and provide lower steady-state error and faster convergence speed than other compared algorithms.

关键词： Signal processing algorithms filtering algorithms Array signal processing Matrices Computational complexity Steady-state Interference Constrained adaptive filtering affine-projection-like M-estimate impulsive noise beamforming

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LEARNING CHARACTERISTICS OF ADAPTIVE LATTICE filtering algorithms

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IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING 1980年第6期28卷 681-691页

作者： GIBSON, CJ HAYKIN, S Communications Research Laboratory McMaster University Hamilton ONT Canada

This paper describes the performance and learning characteristics of the continuously adaptive lattice form for prediction-error filtering. Quantitative relationships are developed for convergence behavior, and procedures are described for selection of the adaptive weighting constant and filter order. Burg's algorithm is used to calculate the reflection coefficients of the filter. Based on this algorithm, two recursive relationships are developed to calculate the coefficients iteratively, one form assuming a stationary input signal, and a more complex form not making this assumption. A quantitative exposition of the convergence behavior in terms of an adaptive weighting constant is set down for these relationships for the first-order filter. Careful attention is given to the decoupling of higher filter orders, leading to the creation of a decoupling constant for the stationary signal case. Higher order convergence and the factors affecting it are examined, resulting in a procedure for choosing the adaptive weighting constant based on the input signal characteristics. Properties of the filter in the spectral domain are also examined. This leads to selection criteria for choosing the filter order, based on the signal characteristics. Application of the filter to the problem of radar clutter discrimination is presented and discussed.

关键词： Lattices filtering algorithms Adaptive filters Signal processing algorithms Convergence Clutter Iterative algorithms Radar signal processing Signal processing Meteorological radar

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COMPARISON OF DISCRETE LINEAR filtering algorithms

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 1973年第1期AE59卷 28-37页

作者： BIERMAN, GJ JET PROP LAB PASADENACA 91103

Seven filter algorithms were presented in a recent survey paper [2], and were compared computationally (operations count) when relatively few observations were to be processed. These algorithms are elaborated further in this paper. Details of the computations are presented, and it is shown that for problems with even moderately large amounts of data, the information matrix and square-root information matrix formulations are computationally more efficient than the other methods considered (conventional Kalman, stabilized Kalman, and square-root covariance mechanizations). It is pointed out that Schmidt's matrix factorization-Householder transformation technique leads to the same equations as those obtained via Potter's method. Several improvements in the equation mechanization are given.

关键词： Maximum likelihood detection filtering algorithms Nonlinear filters Covariance matrix Kalman filters Propulsion Laboratories Equations Information filtering Information filters

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Fourier and walsh digital filtering algorithms for distance protection

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IEEE TRANSACTIONS ON POWER SYSTEMS 1996年第1期11卷 457-462页

作者： Altuve, HJ Diaz, I Vazquez, E Universidad Autonoma de Nuevo Leon San Nicolas de los Garza Nuevo Leon MX Univ. Autonoma de Nuevo Leon Mexico

filtering requirements for distance relays are very critical, because they must estimate precisely and quickly the electrical distance to the fault, even with highly distorted input signals. A number of digital filtering algorithms for distance relays have been proposed and some of them are in use in practical relays;however, power system evolution increases the corruption level of signals and imposes the necessity of continuing the research efforts in this area. In the present paper a comparative evaluation of different digital filtering algorithms for distance protection is performed. An evaluation method is proposed, which gives a comprehensive information about filter transient behavior on a wide frequency range of noise. The discussion is focused in well-known algorithms based on Fourier and Walsh transforms, and includes a recently proposed combined sine-cosine filter.

关键词： filtering algorithms Protective relaying Power system relaying Power system protection Power system transients Distortion Digital relays Power system faults Performance evaluation Information filtering

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IMPLEMENTATION OF DIGITAL filtering algorithms USING PIPELINED VECTOR PROCESSORS

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PROCEEDINGS OF THE IEEE 1987年第9期75卷 1293-1303页

作者： SUNG, W MITRA, SK Signal Processing Laboratory Department of Electrical and Computer Engineering University of California Santa Barbara CA USA

The implementation of digital filtering algorithms using pipelined vector processors is investigated. Modeling of vector processors and vectorization methods are explained, and then the performances of several implementation methods are evaluated based on the model. Vector processor implementation of FIR filtering algorithms using the outer product method and the indirect convolution method is evaluated. Recursive and adaptive filtering algorithms, which lead to dependency problems in direct vector processor implementations, are implemented very efficiently using a newly developed vectorization method. The proposed method computes multiple output samples at a time, making the vector length independent of the filter order. Illustrative examples comparing theoretical results with Cray X-MP simulation results are included.

关键词： filtering algorithms Vector processors Signal processing algorithms Finite impulse response filter Adaptive filters Supercomputers Computational modeling Multidimensional signal processing Signal processing Pipeline processing

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