Neutron-Gamma Classification by Evolutionary Fuzzy Rules and Support Vector Machines

Accurate and fast methods for neutron-gamma discrimination play an essential role in the development of digital scintillation detectors. Digital detectors allow the use of state-of-the-art data analysis, mining, and classification methods in place of traditional approaches based on analog technology...

Full description

Saved in:

Bibliographic Details
Published in:	2015 IEEE International Conference on Systems, Man, and Cybernetics pp. 2638 - 2642
Main Authors:	Kromer, Pavel, Matej, Zdenek, Musilek, Petr, Prenosil, Vaclav, Cvachovec, Frantiek
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-10-2015
Subjects:	evolutionary fuzzy rules Gamma-rays Genetic programming Information retrieval Neutron-gamma classification Neutrons Photonics Support vector machines Training
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Accurate and fast methods for neutron-gamma discrimination play an essential role in the development of digital scintillation detectors. Digital detectors allow the use of state-of-the-art data analysis, mining, and classification methods in place of traditional approaches based on analog technology such as the pulse rise-time and charge-comparison methods. This work compares the ability of evolutionary fuzzy rules and support vector machines to perform accurate neutron-gamma classification. The accuracy and performance of both investigated methods are evaluated on two real-world data sets.
DOI:	10.1109/SMC.2015.461