A Back Propagation-Type Neural Network Architecture for Solving the Complete n × n Nonlinear Algebraic System of EquationsReport as inadecuate




A Back Propagation-Type Neural Network Architecture for Solving the Complete n × n Nonlinear Algebraic System of Equations - Download this document for free, or read online. Document in PDF available to download.

The objective of this research is the presentation of a neural networkcapable of solving complete nonlinear algebraic systems of n equations with n unknowns. The proposed neural solver uses the classical back propagationalgorithm with the identity function as the output function, and supports thefeature of the adaptive learning rate for the neurons of the second hiddenlayer. The paper presents the fundamental theory associated with this approachas well as a set of experimental results that evaluate the performance andaccuracy of the proposed method against other methods found in the literature.

KEYWORDS

Nonlinear Algebraic Systems, Neural Networks, Back Propagation, Numerical Analysis, Computational Methods

Cite this paper

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.





Author: Konstantinos Goulianas1, Athanasios Margaris2, Ioannis Refanidis3, Konstantinos Diamantaras1, Theofilos Papadimitriou4

Source: http://www.scirp.org/



DOWNLOAD PDF




Related documents