Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGoulianas, Konstantinos-
dc.contributor.authorMargaris, Athanasios-
dc.contributor.authorRefanidis, Ioannis-
dc.contributor.authorDiamantaras, Konstantinos I.-
dc.description.abstractThis paper proposes a neural network architecture for solving systems of non-linear equations. A back propagation algorithm is applied to solve the problem, using an adaptive learning rate procedure, based on the minimization of the mean squared error function defined by the system, as well as the network activation function, which can be linear or non-linear. The results obtained are compared with some of the standard global optimization techniques that are used for solving non-linear equations systems. The method was tested with some well-known and difficult applications (such as Gauss–Legendre 2-point formula for numerical integration, chemical equilibrium application, kinematic application, neuropsychology application, combustion application and interval arithmetic benchmark) in order to evaluate the performance of the new approach. Empirical results reveal that the proposed method is characterized by fast convergence and is able to deal with high-dimensional equations systems.en_US
dc.publisherCambridge University Pressen_US
dc.sourceEuropean Journal of Applied Mathematicsen_US
dc.subjectFRASCATI::Natural sciences::Computer and information sciencesen_US
dc.subject.otherNeural networksen_US
dc.subject.otherpolynomial systemsen_US
dc.subject.othernumerical analysisen_US
dc.titleSolving polynomial systems using a fast adaptive back propagation-type neural network algorithmen_US
dc.contributor.departmentΤμήμα Εφαρμοσμένης Πληροφορικήςen_US
Appears in Collections:Department of Applied Informatics

Files in This Item:
File Description SizeFormat 
EJAM-D-16-00169_R1 - preprint.pdfpreprint (revision 1)334,04 kBAdobe PDFThumbnail

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