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|Title:||A Back Propagation-Type Neural Network Architecture for Solving the Complete nxn Nonlinear Algebraic System of Equations|
Diamantaras, Konstantinos I.
FRASCATI::Natural sciences::Computer and information sciences
|Keywords:||Nonlinear Algebraic Systems|
|Publisher:||Scientific Research, Open access|
|Source:||Advances in Pure Mathematics|
|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.|
|Appears in Collections:||Department of Applied Informatics |
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|APM_2016053116554726.pdf||publisher version||1,24 MB||Adobe PDF||View/Open|
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