摘要
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.
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.
作者
Konstantinos Goulianas
Athanasios Margaris
Ioannis Refanidis
Konstantinos Diamantaras
Theofilos Papadimitriou
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;Department of Economics, Democritus University of Thrace, Komotini, Greece)
