Please use this identifier to cite or link to this item:
Title: An adaptive learning rate backpropagation-type neural network for solvingn×nsystems on nonlinear algebraic equations
Authors: Goulianas, K.
Margaris, A.
Refanidis, I.
Diamantaras, K.
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: non-linear algebraic systems
numerical analysis
subclass 65H10
Issue Date: 2016
Publisher: Wiley
Source: Mathematical Methods in the Applied Sciences
Volume: 39
Issue: 10
First Page: 2602
Last Page: 2616
Abstract: This paper presents an MLP‐type neural network with some fixed connections and a backpropagation‐type training algorithm that identifies the full set of solutions of a complete system of nonlinear algebraic equations with n equations and n unknowns. The proposed structure is based on a backpropagation‐type algorithm with bias units in output neurons layer. Its novelty and innovation with respect to similar structures is the use of the hyperbolic tangent output function associated with an interesting feature, the use of adaptive learning rate for the neurons of the second hidden layer, a feature that adds a high degree of flexibility and parameter tuning during the network training stage. The paper presents the theoretical aspects for this approach as well as a set of experimental results that justify the necessity of such an architecture and evaluate its performance.
ISSN: 0170-4214
Electronic ISSN: 1099-1476
Other Identifiers: 10.1002/mma.3715
Appears in Collections:Department of Applied Informatics

Files in This Item:
File Description SizeFormat 
nonlinear-nxn-17.pdfpre-print225,1 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.