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GENERALIZED REGRESSION NEURAL NETWORK BASED EFFICIENT MEMRISTOR MODELING

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dc.contributor.authorCam, Zehra Gulru
dc.contributor.authorCimen, Sibel
dc.contributor.authorSedef, Herman
dc.date.accessioned2026-06-27T13:57:52Z
dc.date.issued2016
dc.description.abstractWith the recent advances in memristors as a potential building block for future hardware, it becomes an important and timely topic to study on memristor modelling. Memristor models are important for designers to exhibit memristor behavior since memristor is not yet available in market. An ideal memristor behavior has been remodel with Generalized Regression Neural Network (GRNN) and presented in this paper. Mathematical equations are used with a set of given memristor process parameters such as R-ON, R-OFF, thickness of TiO2, and instantaneous memristor behaviour is modelled. The behavior of this model is in agreement with the calculations of HP Lab's and Joglekar's SPICE model.en
dc.identifier.urihttps://hdl.handle.net/20.500.14981/55985
dc.identifier.wos000387435600041
dc.language.isoeng
dc.publisherIEEE
dc.relation.conference10th International Conference on Intelligent Systems and Control (ISCO)
dc.relation.ispartofPROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND CONTROL (ISCO'16)
dc.subjectmemristor
dc.subjectmodeling
dc.subjectgeneralized regression neural network
dc.subjectAutomation & Control Systems
dc.subjectComputer Science
dc.subjectEngineering
dc.titleGENERALIZED REGRESSION NEURAL NETWORK BASED EFFICIENT MEMRISTOR MODELING
dc.typeProceedings Paper
dspace.entity.typePublication
local.import.sourceWOS

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