MODEL-FREE DECONVOLUTION OF TRANSIENT SIGNALS USING GENETIC ALGORITHMS

In this paper, the authors have developed a deconvolution procedure, which does not make use of a pre-supposed functional form of the transient signal. Instead, it is based on the inversion of the distortion effects of convolution, which means temporal compression, amplitude enhancement, increasing...

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Bibliographic Details
Published inInternational journal of evolution equations Vol. 8; no. 1; p. 41
Main Authors Bengi, László, Kovács, Balázs, Bezdek, Máté, Keszei, Erno
Format Journal Article
LanguageEnglish
Published Hauppauge Nova Science Publishers, Inc 01.01.2013
Subjects
Cutting
Decay
Deconvolution
Estimates
Fitness
Genetic algorithms
Inversions
Steepness
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Summary:In this paper, the authors have developed a deconvolution procedure, which does not make use of a pre-supposed functional form of the transient signal. Instead, it is based on the inversion of the distortion effects of convolution, which means temporal compression, amplitude enhancement, increasing of the steepness of rise and decay of the measured convolved signal, and cutting initial data to zero to reproduce an eventual sudden jump. The authors use additional genetic algorithms to generate an initial population with a relatively high fitness. The initial population is generated from the measured convolved signal in two subsequent stages, each consisting of a genetic algorithm and the check of the potential to improve the fitness of the population during further breeding, as a candidate for the deconvolved data set. The final genetic algorithm performing the main iteration to get the estimate of the deconvolved data is constructed in such a way that it does not enhance either the low-frequency wavy behaviour, or the high frequency noise.
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ISSN:1549-2907