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检索条件"主题词=Projection algorithms"

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Computational issues in alternating projection algorithms for fixed-order control design

Computational issues in alternating projection algorithms fo...

引用

American Control Conference (ACC)

作者： E. Beran K.M. Grigoriadis Department of Automation and Department of Mathematics Technical University of Denmark Lyngby Denmark Department of Mechanical Engineering University of Houston Houston TX USA

Alternating projection algorithms have been introduced recently to solve fixed-order controller design problems described by linear matrix inequalities and non-convex coupling rank constraints. In this work, an extensive numerical experimentation using proposed benchmark fixed-order control design examples is used to indicate the computational efficiency of the method. These results indicate that the proposed alternating projections are effective in obtaining low-order controllers for small and medium order problems.

关键词： projection algorithms Control design MATLAB Algorithm design and analysis Packaging Open loop systems Design automation Mathematics Mechanical engineering Automatic control

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New affine projection algorithms based on Gauss-Seidel method

New affine projection algorithms based on Gauss-Seidel metho...

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International Symposium on Signals, Circuits and Systems (ISSCS)

作者： F. Albu C. Kotropoulos Z. Cernekova I. Pitas Department of informatics Aristotle University of Thessaloniki Thessaloniki Greece

This paper proposes a robust and stable fast affine projection algorithm based on the Gauss-Seidel method, the so called modified Gauss-Seidel fast affine projection algorithm. The proposed algorithm is generalized for simplified Volterra filters as well. The computational complexity of both the modified Gauss-Seidel fast affine projection algorithm and its generalization for simplified Volterra filters is derived and their performance for acoustic echo cancellation is assessed.

关键词： projection algorithms Gaussian processes Financial advantage program Nonlinear filters Echo cancellers Convergence Robustness Informatics Adaptive filters Stochastic resonance

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Steady-state performance of multichannel affine projection algorithms for active noise control

Steady-state performance of multichannel affine projection a...

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European Signal Processing Conference (EUSIPCO)

作者： Miguel Ferrer Alberto Gonzalez Maria de Diego Gema Piñero Universitat Politecnica de Valencia Valencia Valenciana ES Institute of Telecommunications and Multimedia Applications (iTEAM) Universidad Politecnica de Valencia

ISBN: (纸本)9782839904506

In the field of adaptive filtering, it is well known that affine projection (AP) algorithms lead to a good tradeoff between convergence speed and computational load. For multichannel sound control, some computationally efficient AP (fast AP) algorithms have been recently proposed. This paper analyzes the steady-state mean square error of two efficient affine projection algorithms (representative of the different approaches) for multichannel active noise control (ANC) applications based on a different filtering-x scheme: the modified filtered-x affine projection (MFXAP) algorithm (with the modified filtered-x structure embedded) and the filtered-x affine projection (FXAP) algorithm (based on the conventional filtered-x structure). This study depends on energy conservation arguments and does not require a specific signal distribution. The theoretical models derived allow to accurately predict the steady-state performance of the algorithms considered. Simulation results obtained in practical single-channel and multichannel ANC systems validate the analysis and the derived expressions.

关键词： Vectors Prediction algorithms Steady-state projection algorithms Noise Adaptation models Convergence

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Variable step-size NLMS and affine projection algorithms with variable smoothing factor

Variable step-size NLMS and affine projection algorithms wit...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Tao Dai B. Shahrrava Department of Electrical and Computer Engineering University of Windsor Windsor ONT Canada

In this paper, we propose two new variable step-size normalized least mean square (NLMS) and affine projection algorithms (APA) with variable smoothing factor. Simulation results illustrate that the proposed algorithms have faster convergence rate and lower misadjustment error than the existing algorithms with fixed smoothing factor.

关键词： projection algorithms Smoothing methods Signal processing algorithms Adaptive filters Convergence Adaptive signal processing Least squares approximation Computational modeling Computer errors Signal processing

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Random minibatch projection algorithms for convex feasibility problems

Random minibatch projection algorithms for convex feasibilit...

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IEEE Annual Conference on Decision and Control

作者： Angelia Nedic Ion Necoara Electrical Computer and Energy Engineering Department Arizona State University Tempe AZ USA Automatic Control and Systems Engineering Department University Politehnica Bucharest Bucharest Romania

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

This paper deals with the convex feasibility problem where the feasible set is given as the intersection of a (possibly infinite) number of closed convex sets. We assume that each set is specified algebraically as a convex inequality, where the associated convex function may be even non-differentiable. We present and analyze a random minibatch projection algorithm using special subgradient iterations for solving the convex feasibility problem described by the functional constraints. The updates are performed based on parallel random observations of several constraint components. For this minibatch method we derive asymptotic convergence results and, under some linear regularity condition for the functional constraints, we prove linear convergence rate. We also derive conditions under which the rate depends explicitly on the minibatch size. To the best of our knowledge, this work is the first proving that random minibatch subgradient based projection updates have a better complexity than their single-sample variants.

关键词： Convergence Convex functions projection algorithms Random variables Complexity theory Indexes Tools

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Steady-state and transient analysis of multichannel filtered-x affine projection algorithms

Steady-state and transient analysis of multichannel filtered...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： A. Carini G.L. Sicuranza STI University of Urbino Urbino Italy DEEI University of Trieste Trieste Italy

The paper provides an analysis of the steady-state and transient behavior of two filtered-x affine projection algorithms suitable for multichannel active noise control. The analysis relies on energy conservation arguments, it does not apply the independence theory nor does it impose any restriction to the signal distributions. The paper also shows that filtered-x affine projection algorithms always provide a biased estimate of the minimum-mean-square solution of the active noise control problem.

关键词： Steady-state Transient analysis projection algorithms Active noise reduction Algorithm design and analysis Signal analysis Vectors Symmetric matrices Energy conservation Acoustic noise

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Convex set theoretic image recovery with inexact projection algorithms

Convex set theoretic image recovery with inexact projection ...

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IEEE International Conference on Image Processing

作者： P.L. Combettes Laboratoire d'Analyse Numerique Université Pierre et Marie Curie Paris France

In image recovery, convex projection methods have been in use for almost two decades. However, while it is well known that projections can seldom be computed exactly, the effect of inexact projections on the behavior of such methods has not yet been investigated. We propose such an analysis and establish conditions on the projection errors under which the theoretical convergence properties of various algorithms remain valid. Our analysis covers sequential, parallel, and block-iterative (subgradient) projection methods for consistent and inconsistent set theoretic image recovery problems. It is shown in particular that parallel projection methods are more robust to errors than sequential methods such as the popular POCS (projection on to convex sets) algorithm.

关键词： projection algorithms Convergence Extrapolation Algorithm design and analysis Image analysis Robustness Image restoration Image reconstruction Hilbert space Constraint theory

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Convergence analysis results for the class of affine projection algorithms

Convergence analysis results for the class of affine project...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： S.G. Sankaran A.A. Beex The Bradley Department of Electrical and Computer Engineering Systems Group-DSP Research Laboratory Blacksburg VA USA

Over the last decade, a class of equivalent algorithms called the affine projection class of algorithms, which accelerate the convergence of the normalized LMS (NLMS) algorithm, has been discovered independently. The APA algorithms update weight estimates on the basis of multiple input signal vectors. In this paper, we present the results of the convergence analysis of the APA class of algorithms using a simple model for the input signal vectors. Conditions for convergence of the algorithms are presented. The convergence rate of APA is exponential, and it improves as the number of input signal vectors used for adaptation is increased. However, the rate of improvement in performance (time-to-steady-state) diminishes as the number of input signal vectors increases. For a given convergence rate, APA algorithms exhibit less misadjustment (steady state error) than NLMS. Simulation results are provided to corroborate the analytical results.

关键词： Convergence Algorithm design and analysis projection algorithms Equations Pollution measurement Acceleration Least squares approximation Signal analysis Analytical models Noise measurement

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Adaptive beamforming using affine projection algorithms

Adaptive beamforming using affine projection algorithms

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International Conference on Signal Processing Proceedings (ICSP)

作者： Y.R. Zheng R.A. Goubran Department of Systems and Computer Engineering Carleton University Ottawa ONT Canada

The dynamic adaptive linear constrained minimum variance (LCMV) beamformer generally uses the normalized LMS (NLMS) algorithm for its robustness and simplicity, but it suffers from a low convergence rate. This paper proposes an alternative beamformer incorporating a simplified fast affine projection (FAP) algorithm, which provides fast convergence speed and low computational complexity. The family of affine projection (AP) algorithms improves its convergence speed significantly due to its decorrelation property. We compare the decorrelation properly of the AP algorithms to that of the spatial smoothing algorithm. The fundamental difference between the two decorrelation properties is clarified.

关键词： Array signal processing projection algorithms Decorrelation Least squares approximation Convergence Financial advantage program Interference Resonance light scattering Phased arrays Systems engineering and theory

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STABILITY AND MSE ANALYSES OF AFFINE projection algorithms FOR SPARSE SYSTEM IDENTIFICATION

STABILITY AND MSE ANALYSES OF AFFINE PROJECTION ALGORITHMS F...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Markus V.S. Lima Iker Sobron Wallace A. Martins Paulo S.R. Diniz Universidade Federal do Rio de Janeiro COPPE & DEL-Poli/UFRJ P.O. Box 68504 Rio de Janeiro RJ 21941-972 Brazil

ISBN: (纸本)9781479928941

We analyze two algorithms, viz. the affine projection algorithm for sparse system identification (APA-SSI) and the quasi APA-SSI (QAPA-SSI), regarding their stability and steady-state mean-squared error (MSE). These algorithms exploit the sparsity of the involved signals through an approximation of the l~0 norm. Such approach yields faster convergence and reduced steady-state MSE, as compared to algorithms that do not take the sparse nature of the signals into account. In addition, modeling sparsity via such approximation has been consistently verified to be superior to the widely used l~1 norm in several scenarios. In this paper, we show how to properly set the parameters of the two aforementioned algorithms in order to guarantee convergence, and we derive closed-form theoretical expressions for their steady-state MSE. A key conclusion from the proposed analysis is that the MSE of these two algorithms is a monotonically decreasing function of the sparsity degree. Simulation results are used to validate the theoretical findings.

关键词： Mean square error System identification projection algorithms algorithms approximation Steady state Converge

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