TITLE:
A Back Propagation-Type Neural Network Architecture for Solving the Complete n × n Nonlinear Algebraic System of Equations
AUTHORS:
Konstantinos Goulianas, Athanasios Margaris, Ioannis Refanidis, Konstantinos Diamantaras, Theofilos Papadimitriou
KEYWORDS:
Nonlinear Algebraic Systems, Neural Networks, Back Propagation, Numerical Analysis, Computational Methods
JOURNAL NAME:
Advances in Pure Mathematics,
Vol.6 No.6,
May
31,
2016
ABSTRACT: The objective of this research is the presentation of a neural network
capable of solving complete nonlinear algebraic systems of n equations with n unknowns. The proposed neural solver uses the classical back propagation
algorithm with the identity function as the output function, and supports the
feature of the adaptive learning rate for the neurons of the second hidden
layer. The paper presents the fundamental theory associated with this approach
as well as a set of experimental results that evaluate the performance and
accuracy of the proposed method against other methods found in the literature.