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

被引：1

作者：

Kroemer, Pavel ^{[1
,2
]}

Matej, Zdenek ^{[3
]}

Musilek, Petr ^{[2
,4
]}

Prenosil, Vaclav ^{[3
]}

Cvachovec, Frantisek ^{[3
]}

机构：

[1] VSB Tech Univ Ostrava, IT4Innovat, Ostrava, Czech Republic

[2] VSB Tech Univ Ostrava, Dept Comp Sci, Ostrava, Czech Republic

[3] Masaryk Univ, Fac Informat, Brno 60200, Czech Republic

[4] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB, Canada

来源：

2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS | 2015年

关键词：

Neutron-gamma classification; evolutionary fuzzy rules; support vector machines; FPGA IMPLEMENTATION; FRAMEWORK; SYSTEMS;

D O I：

10.1109/SMC.2015.461

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：2638 / 2642

页数：5

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