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dc.contributor.authorAnsari, Mohammad Samar
dc.date.accessioned2022-09-23T11:10:32Z
dc.date.available2022-09-23T11:10:32Z
dc.date.issued2022-01-25
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/627191/SLE_Single_Layer.pdf?sequence=1
dc.identifier.citationAnsari, M. S. (2022). A single-layer asymmetric RNN with low hardware complexity for solving linear equations. Neurocomputing, 485, 74-88. https://doi.org/10.1016/j.neucom.2022.01.033en_US
dc.identifier.issn0925-2312
dc.identifier.doi10.1016/j.neucom.2022.01.033
dc.identifier.urihttp://hdl.handle.net/10034/627191
dc.description.abstractA single layer neural network for the solution of linear equations is presented. The proposed circuit is based on the standard Hopfield model albeit with the added flexibility that the interconnection weight matrix need not be symmetric. This results in an asymmetric Hopfield neural network capable of solving linear equations. PSPICE simulation results are given which verify the theoretical predictions. A simple technique to incorporate re-configurability into the circuit for setting the different weights of the interconnection is also included. Experimental results for circuits set up to solve small problems further confirm the operation of the proposed circuit.en_US
dc.publisherElsevieren_US
dc.relation.urlhttps://www.sciencedirect.com/science/article/abs/pii/S0925231222000509en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.subjectArtificial Neural Network (ANN)en_US
dc.subjectSingle Layer Neural Networken_US
dc.subjectLinear Equationsen_US
dc.subjectAsymmetric Hopfield Networksen_US
dc.subjectHardware Neural Circuitsen_US
dc.subjectDiagonally Dominant Linear Equationsen_US
dc.titleA single-layer asymmetric RNN with low hardware complexity for solving linear equationsen_US
dc.typeArticleen_US
dc.identifier.eissn1872-8286en_US
dc.contributor.departmentUniversity of Chesteren_US
dc.identifier.journalNeurocomputingen_US
or.grant.openaccessYesen_US
rioxxterms.funderUnfundeden_US
rioxxterms.identifier.projectUnfundeden_US
rioxxterms.versionAMen_US
rioxxterms.versionofrecord10.1016/j.neucom.2022.01.033en_US
rioxxterms.licenseref.startdate2024-01-25
dcterms.dateAccepted2022-01-14
rioxxterms.publicationdate2022-01-25
dc.date.deposited2022-09-23en_US


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