Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/494
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dc.contributor.authorGoulianas, Konstantinos-
dc.contributor.authorMargaris, Athanasios-
dc.contributor.authorRefanidis, Ioannis-
dc.contributor.authorDiamantaras, Konstantinos I.-
dc.date.accessioned2019-11-29T09:27:12Z-
dc.date.available2019-11-29T09:27:12Z-
dc.date.issued2018-04-
dc.identifier10.1017/S0956792517000146en_US
dc.identifier.issn0956-7925en_US
dc.identifier.urihttps://doi.org/10.1017/S0956792517000146en_US
dc.identifier.urihttps://ruomo.lib.uom.gr/handle/7000/494-
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.language.isoenen_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.typeArticleen_US
dc.contributor.departmentΤμήμα Εφαρμοσμένης Πληροφορικήςen_US
local.identifier.volume29en_US
local.identifier.issue2en_US
local.identifier.firstpage301en_US
local.identifier.lastpage337en_US
local.identifier.eissn1469-4425en_US
Appears in Collections:Department of Applied Informatics

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