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Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments

Autor
Tahat, A.; Marti, J.; Khwaldeh, A.; Tahat, K.
Tipus d'activitat
Article en revista
Revista
Chinese Physics B
Data de publicació
2014-02-10
Volum
23
Número
4
Pàgina inicial
046101-1
Pàgina final
046101-12
DOI
10.1088/1674-1056/23/4/046101
Repositori
http://hdl.handle.net/2117/21794 Obrir en finestra nova
URL
http://cpb.iphy.ac.cn/EN/abstract/abstract58541.shtml Obrir en finestra nova
Resum
In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing to classify the proton motion into two categories: transfer‘occurred’and transfer‘not occurred’. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases....
Citació
Tahat, A. [et al.]. Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments. "Chinese Physics B", 10 Febrer 2014, vol. 23, núm. 4, p. 046101-1-046101-12.
Paraules clau
artificial neural network, chart pattern, data mining, empirical valence bond, neural networks, pattern recognition, proton transfer, water environments
Grup de recerca
SIMCON - First-principles approaches to condensed matter physics: quantum effects and complexity

Participants

  • Tahat, Amani  (autor)
  • Marti Rabassa, Jordi  (autor)
  • Khwaldeh, Ali  (autor)
  • Tahat, Kaher  (autor)