Neuro-Fuzzy Recognition System for Detecting Wave Patterns Using Wavelet Coefficients
Summary :
Recognition of specified wave patterns in one-dimensional signals is an important task in many application areas such as computer science, medical science, and geophysics. Many researchers have tried to automate this task with various techniques, recently the soft computing algorithms. This paper proposes a new neuro-fuzzy recognition system for detecting one-dimensional wave patterns using wavelet coefficients as features of the signals and evolution strategy as the training algorithm of the system. The neuro-fuzzy recognition system first trains the wavelet coefficients of the training wave patterns and then evaluates the degree of matching between test wave patterns and the training wave patterns. This system was applied to picking first arrival events in seismic data. Experimental results with three seismic data showed that the system was very successful in terms of learning speed and performances.
- Publication
- IEICE TRANSACTIONS on Information Vol.E84-D No.8 pp.1085-1093
- Publication Date
- 2001/08/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Pattern Recognition
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Sung Hoon JUNG, Doo Sung LEE, "Neuro-Fuzzy Recognition System for Detecting Wave Patterns Using Wavelet Coefficients" in IEICE TRANSACTIONS on Information,
vol. E84-D, no. 8, pp. 1085-1093, August 2001, doi: .
Abstract: Recognition of specified wave patterns in one-dimensional signals is an important task in many application areas such as computer science, medical science, and geophysics. Many researchers have tried to automate this task with various techniques, recently the soft computing algorithms. This paper proposes a new neuro-fuzzy recognition system for detecting one-dimensional wave patterns using wavelet coefficients as features of the signals and evolution strategy as the training algorithm of the system. The neuro-fuzzy recognition system first trains the wavelet coefficients of the training wave patterns and then evaluates the degree of matching between test wave patterns and the training wave patterns. This system was applied to picking first arrival events in seismic data. Experimental results with three seismic data showed that the system was very successful in terms of learning speed and performances.
URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_8_1085/_p
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@ARTICLE{e84-d_8_1085,
author={Sung Hoon JUNG, Doo Sung LEE, },
journal={IEICE TRANSACTIONS on Information},
title={Neuro-Fuzzy Recognition System for Detecting Wave Patterns Using Wavelet Coefficients},
year={2001},
volume={E84-D},
number={8},
pages={1085-1093},
abstract={Recognition of specified wave patterns in one-dimensional signals is an important task in many application areas such as computer science, medical science, and geophysics. Many researchers have tried to automate this task with various techniques, recently the soft computing algorithms. This paper proposes a new neuro-fuzzy recognition system for detecting one-dimensional wave patterns using wavelet coefficients as features of the signals and evolution strategy as the training algorithm of the system. The neuro-fuzzy recognition system first trains the wavelet coefficients of the training wave patterns and then evaluates the degree of matching between test wave patterns and the training wave patterns. This system was applied to picking first arrival events in seismic data. Experimental results with three seismic data showed that the system was very successful in terms of learning speed and performances.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - Neuro-Fuzzy Recognition System for Detecting Wave Patterns Using Wavelet Coefficients
T2 - IEICE TRANSACTIONS on Information
SP - 1085
EP - 1093
AU - Sung Hoon JUNG
AU - Doo Sung LEE
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E84-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2001
AB - Recognition of specified wave patterns in one-dimensional signals is an important task in many application areas such as computer science, medical science, and geophysics. Many researchers have tried to automate this task with various techniques, recently the soft computing algorithms. This paper proposes a new neuro-fuzzy recognition system for detecting one-dimensional wave patterns using wavelet coefficients as features of the signals and evolution strategy as the training algorithm of the system. The neuro-fuzzy recognition system first trains the wavelet coefficients of the training wave patterns and then evaluates the degree of matching between test wave patterns and the training wave patterns. This system was applied to picking first arrival events in seismic data. Experimental results with three seismic data showed that the system was very successful in terms of learning speed and performances.
ER -
English
