Multi component signal decomposition based on chirplet pursuit and genetic algorithms

Zanjireh, Yousef; Rezaie, Amir Hossein; Amindavar, Hamidreza

doi:10.1016/j.apacoust.2013.05.007

Cited by 13 publications

(1 citation statement)

References 23 publications

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“…To overcome the above drawback, the optimization methods have been introduced into MP algorithm. An improved genetic algorithm (GA) is proposed to simplify the calculation of MP algorithm in [11]; however, due to GA's prematurity, the stable capability of learning machine is not good. In [12], a quantum genetic algorithm is introduced for modifying MP process; however, the computing time cannot reduce effectively due to the slow search speed of quantum genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Intuitionistic Fuzzy Kernel Matching Pursuit Based on Particle Swarm Optimization for Target Recognition

Lei

Yue

et al. 2015

Mathematical Problems in Engineering

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In order to overcome the long training time caused by searching optimal basic functions based on greedy strategy from a redundant basis function dictionary for the intuitionistic fuzzy kernel matching pursuit (IFKMP), the particle swarm optimization algorithm with powerful ability of global search and quick convergence rate is applied to speed up searching optimal basic function data in function dictionary. The approach of intuitionistic fuzzy kernel matching pursuit based on particle swarm optimization algorithm, namely, PS-IFKMP, is proposed. This algorithm is applied to the aerospace target recognition, which requires real-time ability. Simulation results show that, compared with the conventional approaches, the proposed algorithm can decrease training time and improve calculation efficiency obviously with almost unchanged classification accuracy, while the model has better sparsity and generalization. It is also demonstrated that this approach is suitable for the application requiring both accuracy and efficiency.

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Section: Introductionmentioning

confidence: 99%