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检索条件"主题词=Pursuit Algorithms"

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Interpretation and convergence speed of two recently introduced adaptive filters (FEDS/RAMP)

Interpretation and convergence speed of two recently introdu...

引用

IEEE Region 10 International Conference TENCON

作者： J.H. Husoy M.S.E. Abadi Department of Electrical Engineering Stavanger University College Stavanger Norway Department of Electrical Engineering Tarbiat Moalem University Tehran Iran

Fast Euclidean direction search (FEDS) (T. Bose et al., 2002) and recursive adaptive matching pursuit (RAMP) (J.H. Husoy, 2003) are two recently introduced algorithms for adaptive filtering characterized by low computational complexity, good convergence, and numerical robustness. While conceived from quite different perspectives, we point out in the first part of the paper, that both algorithms are closely related and can be interpreted as different variants of 1) a matching pursuit procedure applied to a particular over determined equation set, 2) a constrained least squares (LS) optimization problem, and 3) a Gauss-Seidel like iterative solution procedure applied to a normal equation. Both FEDS and RAMP have been demonstrated experimentally to possess excellent convergence behavior in several application scenarios. However, a tool for predicting the convergence of these algorithms based on second order statistics is lacking. In the second part of the paper such a tool is presented. Finally, we present simulation results showing good agreement between predicted and actual learning curves for both FEDS and RAMP.

关键词： Adaptive filters Matching pursuit algorithms Iterative algorithms Differential equations Filtering algorithms Computational complexity Convergence of numerical methods Robustness pursuit algorithms Least squares methods

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Diffusion equations for adaptive affine distributions

Diffusion equations for adaptive affine distributions

引用

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

作者： J. Gosme C. Richard P. Goncalves LM2S Université de Technologie de Troyes Troyes France INRIA-Rhône-Alpes MISTIS IST-ISR Lisboa Portugal

We propose an extension of the adaptive diffusion technique for time-frequency representations proposed by P. Goncalves and E. Payot (see Proc. IEEE Digital Sig. Process. Workshop, 1998). Instead of processing time-frequency representations and keeping the covariance with respect to time and frequency shifts untouched, our adaptive filtering technique processes time-scale representations of the affine class while preserving the covariance properties of such representations. In order to obtain representations with improved readability, we aim at removing cumbersome interference terms while not blurring the signal terms. We show that the association of a conductance function to our diffusion scheme can make significant improvement toward reaching this goal. Indeed, a conductance function provides a way to adapt locally the amount of smoothing to the representation. Note that the adaptivity of this affine technique is not based on any waveform dictionary, such as matching pursuit algorithms.

关键词： Equations Time frequency analysis Signal analysis Smoothing methods Dictionaries Matching pursuit algorithms Frequency modulation Adaptive filters Interference pursuit algorithms

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Shapiro-Wilk index: discriminatory index

Shapiro-Wilk index: discriminatory index

引用

International Symposium on Communications and Information Technologies (ISCIT)

作者： J. Chinrungrueng F.G. Meyert Signal Processing for Telecommunication Section Pathumthani Thailand

We propose to employ the Shapiro-Wilk statistic (W statistic) as discriminatory index in the best clustering basis algorithm. The original work on the best clustering basis requires a clustering algorithm to partition clusters of data in order to obtain discriminatory index associated to a wavelet packet. The use of the W statistic eliminates the need for a clustering algorithm and therefore reduces the computational complexity introduced by a clustering algorithm. The use of the W statistic is based on testing for normality of data projection. The relationship between the W statistic and distance between cluster centroids is provided empirically. The experiments performed show that the algorithm with the W statistic can find basis functions with large discriminatory powers.

关键词： Signal processing algorithms Statistics Clustering algorithms Partitioning algorithms pursuit algorithms Fourier transforms Matching pursuit algorithms Wavelet packets Entropy Time series analysis

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An empirical evaluation of interval estimation for Markov decision processes

An empirical evaluation of interval estimation for Markov de...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： A.L. Strehl M.L. Littman Department of Computer Science Rutgers University Piscataway NJ USA

This work takes an empirical approach to evaluating three model-based reinforcement-learning methods. All methods intend to speed the learning process by mixing exploitation of learned knowledge with exploration of possibly promising alternatives. We consider /spl epsi/-greedy exploration, which is computationally cheap and popular, but unfocused in its exploration effort; R-Max exploration, a simplification of an exploration scheme that comes with a theoretical guarantee of efficiency; and a well-grounded approach, model-based interval estimation, that better integrates exploration and exploitation. Our experiments indicate that effective exploration can result in dramatic improvements in the observed rate of learning.

关键词： Computer science State-space methods Arm Learning Convergence Mathematical model Artificial intelligence Sampling methods pursuit algorithms

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A biomimetic VLSI architecture for small target tracking

A biomimetic VLSI architecture for small target tracking

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： V. Pant C.M. Higgins Department of Electrical and Computer Engineering University of Arizona Tucson Tucson AZ USA Arizona Research Laboratories Division of Neurobiology University of Arizona Tucson Tucson AZ USA

Tracking of a target in a cluttered environment requires extensive computational architecture. However, even a small housefly is adept at pursuing its prey. Biomimetic algorithms suggest a way of looking at this problem. In the lobula plate of a fly's brain, a neural circuit is hypothesized based on a tangential cell called the figure detection (FD) cell. The proposed small target fixation algorithm based on electrophysiological recordings does not take into account the translation of the pursuer during pursuit. We have modified the biological algorithm to include this aspect of tracking. In this paper, we present the elaborated biological algorithm for small target tracking, and an analog VLSI implementation of this algorithm.

关键词： Biomimetics Very large scale integration Target tracking pursuit algorithms Object detection Detectors Optical filters Computer architecture Circuits Biology computing

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A comparative study of some simplified RLS-type algorithms

A comparative study of some simplified RLS-type algorithms

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International Symposium on Communications Control and Signal Processing (ISCCSP)

作者： J.H. Husoy M.S.E. Abadi Stavanger University College Stavanger Norway Tarbiat Moalem University Tehran Iran Tarbiat Modarres University Teheran Iran

The recursive least squares (RLS) algorithm has established itself as the "ultimate" adaptive filtering algorithm in the sense that it is the adaptive filter exhibiting the best convergence behavior. Unfortunately, practical implementations of the algorithm are often associated with high computational complexity and/or poor numerical properties. Rather than focusing on full RLS algorithm implementations aiming directly at remedying these problems, we argue that the use of simplified or partial RLS algorithms may be a viable alternative to full RLS. In particular, we point out that two recently introduced algorithms, fast Euclidian direction search (FEDS) and recursive adaptive matching pursuit (RAMP) can indeed be interpreted as such partial RLS algorithms exhibiting a nice tradeoff between complexity and performance. We support our presentation by a comprehensive set of simulation results.

关键词： Resonance light scattering Matching pursuit algorithms Adaptive filters pursuit algorithms Filtering algorithms Computational complexity Convergence Least squares approximation Autocorrelation Educational institutions

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Double cancellation receivers for asynchronous CDMA systems in multipath channels

Double cancellation receivers for asynchronous CDMA systems ...

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IEEE Conference on Vehicular Technology (VTC)

作者： D.K. Borah Xiaoli Liu Klipsch School of Electrical and Computer Engineering New Mexico State University Las Cruces NM USA

Low complexity receiver structures for CDMA systems in multipath channels with a large possible number of channel coefficients are studied. The channel estimator employs a matching pursuit followed by cancellation (MPC) algorithm that consists of two steps. In the first step, a basic matching pursuit algorithm. that identifies the strongest channel taps and successively cancels them, is used. The second step of the algorithm uses the already estimated channel taps to remove interference from the received vector in order to estimate the channel coefficients more accurately. The data detector employs a partial parallel interference canceller (PIC). It is observed that a multipass structure consisting of an MP step and a C step followed by a single stage PIC performs nearly similar to a multipass structure where a multistage PIC is employed after the C step. Further, nonlinear clipped decisions, fed into both the channel estimator and PIC, are found to give good results.

关键词： Multiaccess communication Multipath channels Matching pursuit algorithms Detectors Interference cancellation Bit error rate pursuit algorithms Iterative algorithms Silicon carbide Channel estimation

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Generalized TSE: a new generalized estimator based learning automaton

Generalized TSE: a new generalized estimator based learning ...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： M. Agache B.J. Oommen School of Computer Science Carleton University Ottawa Canada

The fastest learning automata (LA) algorithms currently available fall in the family of estimator algorithms introduced by Thathachar and Sastry. The pioneering work of these authors was the pursuit Algorithm, which pursues only the current estimated optimal action. Later, the same authors introduced a more sophisticated estimator algorithm, known as the TSE algorithm. This paper introduces first a vectorial representation the TSE algorithm that shows more clearly the underlying concepts of the TSE algorithm. Furthermore, using this vectorial representation, we introduce a generalized TSE estimator algorithm (GTSE). We argue that this learning scheme minimizes the probability of pursuing a wrong action and it is proven empirically to be the fastest converging estimator learning algorithm known to date. To attest this, we present a quantitative comparison of its performance against the TSE and other existing continuous estimator algorithms.

关键词： Learning automata pursuit algorithms Probability Computer science Books Feedback State estimation

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Sparse decomposition over multi-component redundant dictionaries

Sparse decomposition over multi-component redundant dictiona...

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IEEE Workshop on Multimedia Signal Processing

作者： L. Granai P. Vandergheynst Signal Processing Institute Swiss Federal Institute of Technology Lausanne Switzerland

In many applications - such as compression, de-noising and source separation - a good and efficient signal representation is characterized by sparsity. This means that many coefficients are close to zero, while only few ones have a non-negligible amplitude. On the other hand, real-world signals such as audio or natural images - clearly present peculiar structures. In this paper we introduce a global optimization framework that aims at respecting the sparsity criterion while decomposing a signal over an overcomplete, multi-component dictionary. We adopt a probabilistic analysis which can lead to consider the signal internal structure. As an example that fits this framework, we propose the weighted basis pursuit algorithm, based on the solution of a convex, non-quadratic problem. Results show that this method can provide sparse signal representations and sparse m-terms approximations. Moreover, weighted basis pursuit provides a faster convergence compared to basis pursuit.

关键词： Dictionaries Signal resolution Noise reduction Source separation Signal representations Signal analysis Signal processing pursuit algorithms Video signal processing Image analysis

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An M-ary KMP classifier for multi-aspect target classification

An M-ary KMP classifier for multi-aspect target classificati...

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

作者： Xuejun Liao Hui Li B. Krishnapuram Department of Electrical and Computer Engineering Duke University USA

The kernel matching pursuit (KMP) algorithm is re-formulated in the framework of the theory of optimal experiments, using a weighted sum of squared errors as the loss function, and it is extended to the case of M-ary target classification and kernel optimization. The M-ary KMP classifier is applied to the multi-aspect classification of moving targets based on high-range resolution (HRR) radar signatures, for which the target-sensor orientations are assumed approximately known. A multi-aspect processing method is presented based on the use of the estimates of target-sensor orientation angles. The KMP classification results for ten MSTAR targets are presented, with a comparison to corresponding results using the relevance vector machine (RVM).

关键词： Matching pursuit algorithms Kernel pursuit algorithms Least squares approximation Computer errors Vectors Dictionaries Least squares methods Doppler radar

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