A genetic matching pursuit algorithm

Ștefănoiu, Dan; lonescu, F.

doi:10.1109/isspa.2003.1224769

Cited by 11 publications

(7 citation statements)

References 3 publications

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“…In ModQEAbased TFAD, m = V k=1 m k , where V is the total number of parameters of the time-frequency atom g γ (t) and m k is the number of binary bits of the kth parameter in g γ (t). For example, if g γ (t) has four parameters u 1 , u 2 , u 3 , u 4 and each of them is represented by 12 Q-bits, a Q-bit individual or a chromosome (9): 37 · · · α k,48 β k,1 β k,2 · · · β k,12 β k,13 · · · β k,24 β k,25 · · · β k,36 β k,37 · · · β k,48 …”

Section: Modqeamentioning

confidence: 99%

“…Ferreira da Silva [7][8][9] proposed evolutionary pursuit to solve signal approximation problems, and experimental results show that the evolutionary pursuit is compared with basis pursuit and matching pursuit with a greedy strategy. Also, the applicability of matching pursuit with CGA was verified successfully in several engineering applications including image coding [10], signal detection [27], fault diagnosis [39], and data denoising and compression [37,38].…”

Section: Introductionmentioning

confidence: 99%

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Time-Frequency Atom Decomposition with Quantum-Inspired Evolutionary Algorithms

Zhang

2009

Circuits Syst Signal Process

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Time-frequency atom decomposition (TFAD) provides a flexible representation for non-stationary signals, but the extremely high computational effort greatly blocks its practical applications. Quantum-inspired evolutionary algorithms (QEA) are efficient optimization methods with strong search capability and rapid convergence. This paper proposes the application of a modified variant of QEA to the TFAD problem. The problem on TFAD with evolutionary algorithms is formulated. By using gray coding, elite groups, and an appropriate termination criterion, the modified QEA is developed to search the suboptimal time-frequency atoms from a very large and redundant time-frequency dictionary. Also, this paper discusses the reduction of the computational time in terms of parameter setting, and presents an application example of radar emitter signals. Extensive experiments show the effectiveness and practicability of the presented algorithm.

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Section: Modqeamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Time-Frequency Atom Decomposition with Quantum-Inspired Evolutionary Algorithms

Zhang

2009

Circuits Syst Signal Process

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“…The second optimisation mode is using optimisation algorithms to improve best atom search of MP. For example, genetic algorithm and tree search algorithm were used [13][14][15][16]. Owing to existence of premature phenomenon and inconsistencies of results by multiple executions, the non-optimal results may appear.…”

Section: Introductionmentioning

confidence: 99%

Atom‐based‐segmented MP compression algorithm for seismic data

Yin

Zhou

2018

IET signal process.

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Data compression is an effective way to improve the seismic data transmission efficiency. The features of seismic exploration are long sampling time and large data quantity, so the compression algorithm should achieve high-fidelity, highcompression ratio (CR) and low-compression time. Since the existing compression algorithms cannot meet the requirements of site-collected seismic data compression, the segmented matching pursuit (SMP) compression algorithm based on new atom dictionary is proposed. This method is based on the principle of MP. A novel-segmented compression structure is adopted. The modified Morlet wave atom dictionary is designed to replace the previous dictionaries. The results of comparative experiments show that the proposed algorithm makes improvements in CR and compression time with the same fidelity requirement.

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“…However, the the necessary dictionary of time-frequency atoms being very large, the computational load turns out to be the main problem of TFAD [2]. After a greedy algorithm was presented in [3], several fast implementation algorithms were introduced in [6][7][8][9]. Although genetic algorithm was used to reduce the computational load in [1,2,[6][7][8], this problem need be studied further by using some unconventional computation methods with some advantages over conventional genetic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Quantum-Inspired Genetic Algorithm Based Time-Frequency Atom Decomposition

Zhang

Rong

2007

Computational Science – ICCS 2007

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Abstract. The main problem of time-frequency atom decomposition (TFAD) lies in an extremely high computational load. This paper presents a fast implementation method based on quantum-inspired genetic algorithm (QGA). Instead of finding the optimal atom in greedy implementation algorithm, this method is to search a satisfactory atom in every iteration of TFAD. Making full use of QGA's advantages such as good global search capability, rapid convergence and short computing time, the method reduces greatly the computational load of TFAD. Experiments conducted on radar emitter signals verify the effectiveness and practicality of the introduced method.

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A genetic matching pursuit algorithm

Cited by 11 publications

References 3 publications

Time-Frequency Atom Decomposition with Quantum-Inspired Evolutionary Algorithms

Time-Frequency Atom Decomposition with Quantum-Inspired Evolutionary Algorithms

Atom‐based‐segmented MP compression algorithm for seismic data

Quantum-Inspired Genetic Algorithm Based Time-Frequency Atom Decomposition

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