Advances in Pure Mathematics
Vol.6 No.6(2016), Paper ID 67010, 26
pages
DOI:10.4236/apm.2016.66033
A Back Propagation-Type Neural Network Architecture for Solving the Complete n × n Nonlinear Algebraic System of Equations
Konstantinos Goulianas, Athanasios Margaris, Ioannis Refanidis, Konstantinos Diamantaras, Theofilos Papadimitriou
TEI of Thessaloniki, Department of Informatics, Thessaloniki, Greece
TEI of Larissa, Department of Computer Science and Engineering, Larissa, Greece
Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece
TEI of Thessaloniki, Department of Informatics, Thessaloniki, Greece
Department of Economics, Democritus University of Thrace, Komotini, Greece
Copyright © 2016 Konstantinos Goulianas, Athanasios Margaris, Ioannis Refanidis, Konstantinos Diamantaras, Theofilos Papadimitriou et al. This is
an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any
medium, provided the original work is properly cited.
How to Cite this Article
Goulianas, K. , Margaris, A. , Refanidis, I. , Diamantaras, K. and Papadimitriou, T. (2016) A Back Propagation-Type Neural Network Architecture for Solving the Complete n × n Nonlinear Algebraic System of Equations.
Advances in Pure Mathematics,
6, 455-480. doi:
10.4236/apm.2016.66033.