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检索条件"主题词=Pursuit Algorithms"

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A study on hierarchical modular reinforcement learning for multi-agent pursuit problem based on relative coordinate states

A study on hierarchical modular reinforcement learning for m...

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IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA)

作者： Tatsuya Wada Takuya Okawa Toshihiko Watanabe Graduate School of Engineering Osaka Electro Communication University Neyagawa Osaka Japan Faculty of Engineering Osaka Electro Communication University Neyagawa Osaka Japan

ISBN: (纸本)9781424448081

In order to realize intelligent agent such as autonomous mobile robots, reinforcement learning is one of the necessary techniques in behavior control system. However, applying the reinforcement learning to actual sized problem, the ¿curse of dimensionality¿ problem in partition of sensory states should be avoided maintaining computational efficiency. In multi-agent reinforcement learning, the problem is emerged owing to the high dimensionality of each agent states. We apply the hierarchical modular reinforcement learning in order to deal with the dimensional problem and task decomposition. In this study, we focus on investigation of the learning performance of agent that represents the input states in relative coordinate system. We show effectiveness of proposed learning algorithm based on relative expressions with limited view through numerical experiments of the pursuit problem.

关键词： Learning Mobile robots pursuit algorithms Intelligent agent Sensor systems Robot sensing systems Robot kinematics Intelligent robots Intelligent sensors Computational efficiency

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Tree-based Multiscale pursuit

Tree-based Multiscale Pursuit

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2009国际通信电路与系统学术会议(ICCCAS 2009)(2009 International Conference on Communications，Circuits and Systems)

作者： Juan Wang Qun Wan Anming Huang Tao Gan Electrical Engineering Department University of Electronic Science and Technology of China (UESTC) Chengdu 610054 China

ISBN: (纸本)9781424448876

This paper introduces a new practical algorithm called tree-based multiscale pursuit (TMSP) for sparse signal representation over redundant dictionary. The main contribution is the construction of the tree-structured multiscale dictionary which is based on the similarity of the atoms. The similarity is estimated by the distance of the atoms’ parameters. Owing to efficiently organizing the original dictionary, TMSP algorithm makes a good compromise between the high performance and efficient implementation, and can satisfy the users’ different performances requirements by simply stopping the pursuit at different levels of the tree. Moreover, the tree-structured multiscale dictionary requires no extra memory and is easier to be constructed when compared with the tree-structured dictionary used in the tree-based pursuit.

关键词： Dictionaries Matching pursuit algorithms pursuit algorithms Computational complexity Gallium nitride Approximation algorithms Signal representations Wavelet transforms Organizing Acceleration

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Bayesian pursuit algorithm for sparse representation

Bayesian Pursuit algorithm for sparse representation

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

作者： H. Zayyani M. Babaie-Zadeh C. Jutten Department of Electrical Engineering and Advanced Communication Research Institute Sharif University of Technology Tehran Iran GIPSA Laboratory Grenoble France

In this paper, we propose a Bayesian pursuit algorithm for sparse representation. It uses both the simplicity of the pursuit algorithms and optimal Bayesian framework to determine active atoms in sparse representation of a signal. We show that using Bayesian Hypothesis testing to determine the active atoms from the correlations leads to an efficient activity measure. Simulation results show that our suggested algorithm has better performance among the algorithms which have been implemented in our simulations in most of the cases.

关键词： Bayesian methods pursuit algorithms Matching pursuit algorithms Atomic measurements Dictionaries Iterative algorithms Signal processing algorithms Sparse matrices Compressed sensing Collaborative work

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ECON: A Kernel Basis pursuit Algorithm with Automatic Feature Parameter Tuning, and its Application to Photometric Solids Approximation

ECON: A Kernel Basis Pursuit Algorithm with Automatic Featur...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Manuel Loth Philippe Preux Samuel Delepoulle Christophe Renaud Team-Project SequeL INRIA Lille Nord France Laboratoire d Informatique du Littoral Université du Littoral Côte d''Opale Calais France

This paper introduces a new algorithm, namely the equi-correlation network (ECON), to perform supervised classification, and regression. ECON is a kernelized LARS-like algorithm, by which we mean that ECON uses an l 1 regularization to produce sparse estimators, ECON efficiently rides the regularization path to obtain the estimator associated to any regularization constant values, and ECON represents the data by way of features induced by a feature function. The originality of ECON is that it automatically tunes the parameters of the features while riding the regularization path. So, ECON has the unique ability to produce optimally tuned features for each value of the constant of regularization. We illustrate the remarkable experimental performance of ECON on standard benchmark datasets; we also present a novel application of machine learning in the field of computer graphics, namely the approximation of photometric solids.

关键词： Kernel pursuit algorithms Photometry Solids Machine learning Europe Tellurium Approximation algorithms Application software Computer graphics

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Dictionary Preconditioning for Orthogonal Matching pursuit in the Presence of Noise

Dictionary Preconditioning for Orthogonal Matching Pursuit i...

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2009国际通信电路与系统学术会议(ICCCAS 2009)(2009 International Conference on Communications，Circuits and Systems)

作者： An-Min Huang Qun Wan Wan-Lin Yang Department of Electronic Engineering University of Electronic Science and Technology of China Chengdu P.R.China

ISBN: (纸本)9781424448876

Since orthogonal matching pursuit (OMP) may fail to identify correct atoms if the cumulative coherence of dictionary is too high, Schnass and Vandergheynst modified OMP algorithm by using a sensing dictionary in sensing step and proposed an method for designing sensing dictionary in noiseless case [4]. However, only contaminated observations are available in most practical situations. In this letter, we provide the sufficient condition under which the modified OMP can be guaranteed to identify correct atoms in noisy case and propose a method to designing sensing dictionary. Finally, simulation results are presented to demonstrate the effectivity of the proposed method.

关键词： Dictionaries Matching pursuit algorithms Design methodology Design engineering pursuit algorithms Algorithm design and analysis Sufficient conditions Counting circuits Helium Inverse problems

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A low complexity Orthogonal Matching pursuit for sparse signal approximation with shift-invariant dictionaries

A low complexity Orthogonal Matching Pursuit for sparse sign...

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

作者： Boris Mailhe Remi Gribonval Frederic Bimbot Pierre Vandergheynst Centre de Recherche INRIA Rennes-Bretagne Atlantique Projet METISS Rennes France School of Engineering EPFL Signal Processing Laboratories Lausanne Switzerland

We propose a variant of orthogonal matching pursuit (OMP), called LoCOMP, for scalable sparse signal approximation. The algorithm is designed for shift-invariant signal dictionaries with localized atoms, such as time-frequency dictionaries, and achieves approximation performance comparable to OMP at a computational cost similar to matching pursuit. Numerical experiments with a large audio signal show that, compared to OMP and gradient pursuit, the proposed algorithm runs in over 500 less time while leaving the approximation error almost unchanged.

关键词： Matching pursuit algorithms Dictionaries Approximation algorithms Greedy algorithms Signal processing algorithms Approximation error Signal processing pursuit algorithms Signal design Algorithm design and analysis

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Golay complementary waveforms for sparse delay-Doppler radar imaging

Golay complementary waveforms for sparse delay-Doppler radar...

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IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

作者： Yuejie Chi Robert Calderbank Ali Pezeshki Princeton University Princeton NJ USA Colorado State University Fort Collins CO USA

ISBN: (纸本)9781424451791;9781424451807

We present a new approach to radar imaging that exploits sparsity in the matched filter domain to enable high resolution imaging of targets in delay and Doppler. We show that the vector of radar cross-ambiguity values at any fixed test delay cell has a sparse representation in a Vandermonde frame that is obtained by discretizing the Doppler axis. The expansion coefficients are given by the auto-correlation functions of the transmitted waveforms. We show that the orthogonal matching pursuit (OMP) algorithm can then be easily used to identify the locations of the radar targets in delay and Doppler. Unambiguous imaging in delay is enabled by alternating between a Golay pair of phase coded waveforms at the transmission to eliminate delay sidelobe effects. We then extend our work to multi-channel radar, by developing a sparse recovery approach for dually-polarimetric radar. We exploit sparsity in a bank of matched filters, each of which is matched to an entry of an Alamouti matrix of Golay waveforms to recover a co-polar or cross-polar polarization scattering component.

关键词： Propagation delay Radar imaging Doppler radar Matching pursuit algorithms Matched filters Delay effects High-resolution imaging Testing Autocorrelation pursuit algorithms

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Sub clustering K-SVD: Size variable dictionary learning for sparse representations

Sub clustering K-SVD: Size variable dictionary learning for ...

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IEEE International Conference on Image Processing

作者： JianZhou Feng Li Song XiaoKang Yang Wenjun Zhang Institute of Image Communication & Information Proc Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai Jiaotong University Shanghai China

Sparse signal representation from overcomplete dictionaries have been extensively investigated in recent research, leading to state-of-the-art results in signal, image and video restoration. One of the most important issues is involved in selecting the proper size of dictionary. However, the related guidelines are still not established. In this paper, we tackle this problem by proposing a so-called sub clustering K-SVD algorithm. This approach incorporates the subtractive clustering method into K-SVD to retain the most important atom candidates. At the same time, the redundant atoms are removed to produce a well-trained dictionary. As for a given dataset and approximation error bound, the proposed approach can deduce the optimized size of dictionary, which is greatly compressed as compared with the one needed in the K-SVD algorithm.

关键词： Dictionaries Matching pursuit algorithms Clustering algorithms pursuit algorithms Signal representations Signal processing Image restoration Signal restoration Guidelines Clustering methods

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Fast implementation of a ℓ1 - ℓ1 regularized sparse representations algorithm.

Fast implementation of a ℓ1 - ℓ1 regularized sparse repres...

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

作者： Jean-Jacques Fuchs IRISA/Université de Rennes 1 Rennes France

When seeking a sparse representation of a signal on a redundant basis, one replaces generally the quest for the true sparsest model by an lscr 1 minimization and solves thus a linear program. In the presence of noise one further replaces the exact reconstruction constraint by an approximate one. The lscr 2 -norm is generally chosen to measure the reconstruction error because of its link with Gaussian noise and the stability and simplicity of the ensuing algorithms, but the lscr 1 -norm may be preferred in some cases when the noise has heavier tails or in the presence of outliers. We propose to replace the usual lscr 2 - lscr 1 regularized criterion by a lscr 1 - lscr 1 regularized criterion and show how to construct a fast dedicated optimization algorithm that solves this criterion in a finite number of steps. Since quite often even these fast optimal programs are considered to be too time consuming, we further develop an ad hoc sub-optimal algorithm that could be called the lscr 1 -matching pursuit algorithm.

关键词： Matching pursuit algorithms Signal processing algorithms pursuit algorithms Gaussian noise Sparse matrices Iterative algorithms Noise measurement Stability Tail Optimization methods

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Sparse channel estimation for multicarrier underwater acoustic communication: From subspace methods to compressed sensing

Sparse channel estimation for multicarrier underwater acoust...

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OCEANS - Europe

作者： Christian R. Berger Shengli Zhou James C. Preisig Peter Willett Department of Electrical and Computer Engineering University of Connecticut Storrs CT USA Applied Ocean Physics and Engineering Department Woods Hole Oceanographic Institution Woods Hole MA USA

In this paper, we present various channel estimators that exploit the channel sparsity in a multicarrier underwater acoustic system, including subspace algorithms from the array precessing literature, namely root-MUSIC and ESPRIT, and recent compressed sensing algorithms in form of Orthogonal Matching pursuit (OMP) and Basis pursuit (BP). Numerical simulation and experimental data of an OFDM block-by-block receiver are used to evaluate the proposed algorithms in comparison to the conventional least-squares (LS) channel estimator. We observe that subspace methods can tolerate small to moderate Doppler effects, and outperform the LS approach when the channel is indeed sparse. On the other hand, compressed sensing algorithms uniformly outperform the LS and subspace methods. Coupled with a channel equalizer mitigating intercarrier interference, the compressed sensing algorithms can handle channels with significant Doppler spread.

关键词： Channel estimation Underwater acoustics Underwater communication Compressed sensing Matching pursuit algorithms pursuit algorithms Acoustic arrays Numerical simulation OFDM Doppler effect

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