Construction of consistent neural network empirical physical formulas for detector counts in neutron exit channel selection

AKKOYUN S., YILDIZ N.

MEASUREMENT, cilt.46, sa.9, ss.3192-3197, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 46 Sayı: 9
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1016/j.measurement.2013.05.024
  • Dergi Adı: MEASUREMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.3192-3197
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

Proper selection of neutron exit channels following heavy-ion reactions is important in nuclear structure physics. A knowledge of detector counts versus number of neutron interaction points per event can be useful in this selection. In this paper, we constructed layered feedforward neural networks (LFNNs) consistent empirical physical formulas (EPFs) to estimate the detector counts versus number of neutron interaction points per event. The LFNN-EPFs are of explicit mathematical functional form. Therefore, by various suitable operations of mathematical analysis, these LFNN-EPFs can be used to derivate further physical functions which might be potentially relevant to neutron exit channel selection. (C) 2013 Elsevier Ltd. All rights reserved.