Time-frequency feature analysis and recognition of fission neutrons signal based on support vector machine

jin jing; wei biao; feng peng; tang yuelin; zhou mi

Volume 22 Issue 10

Sep. 2010

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Article Navigation > High Power Laser and Particle Beams > 2010 > 22(10): 0-

jin jing, wei biao, feng peng, et al. Time-frequency feature analysis and recognition of fission neutrons signal based on support vector machine[J]. High Power Laser and Particle Beams, 2010, 22.

Citation:

jin jing, wei biao, feng peng, et al. Time-frequency feature analysis and recognition of fission neutrons signal based on support vector machine[J]. High Power Laser and Particle Beams, 2010, 22.

jin jing, wei biao, feng peng, et al. Time-frequency feature analysis and recognition of fission neutrons signal based on support vector machine[J]. High Power Laser and Particle Beams, 2010, 22.

Citation:

jin jing, wei biao, feng peng, et al. Time-frequency feature analysis and recognition of fission neutrons signal based on support vector machine[J]. High Power Laser and Particle Beams, 2010, 22.

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Time-frequency feature analysis and recognition of fission neutrons signal based on support vector machine

1.
Key Laboratory of Opto-electronics Technology and System of Ministry of Education,Department of Opto-electronics Engineering,Chongqing University,Chongqing 400044,China

Publish Date: 2010-09-03

Abstract

Abstract

Based on the interdependent relationship between fission neutrons(252Cf)) and fission chain((235U system), the paper presents the time-frequency feature analysis and recognition in fission neutron signal based on support vector machine(SVM) through the analysis on signal characteristics and the measuring principle of the 252 fission neutron signal. The time-frequency characteristics and energy features of the fission neutron signal are extracted by using wavelet decomposition and de-noising wavelet packet decomposition, and then applied to training and classification by means of support vector machine based on statistical learning theory. The results show that, it is effective to obtain features of nuclear signal via wavelet decomposition and de-noising wav
- fission neutron source,
- wavelet packet decomposition,
- feature extraction,
- support vector machine,
- classification and recognition

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