Publication details
Neutron-Gamma Classification by Evolutionary Fuzzy Rules and Support Vector Machines
| Authors | |
|---|---|
| Year of publication | 2015 |
| Type | Article in Proceedings |
| Conference | 2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS |
| MU Faculty or unit | |
| Citation | |
| Web | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7379593&tag=1 |
| Doi | http://dx.doi.org/10.1109/SMC.2015.461 |
| Field | Informatics |
| Keywords | fuzzy logic; neutron; spectrometry |
| Attached files | |
| Description | 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. |