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A Neural Network-Based Design Automation of a Second Generation Current Conveyor

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dc.contributor.authorKahraman, Nihan
dc.contributor.authorKiyan, Tuba
dc.date.accessioned2026-06-27T13:41:57Z
dc.date.issued2014
dc.description.abstractAn artificial neural network approach for the automated design of a positive type second generation current conveyor is presented in this paper. A multi-layer perceptron structure is successfully employed to estimate the corresponding transistor dimensions for a given set of desired performance criteria of the circuit. Data generated by a circuit simulation program (SPICE) is used to train the artificial neural network. The excellent agreement between the desired specifications and the actual results from SPICE simulation results approves that neural networks are powerful tools for automated analog circuit sizing.en
dc.description.urihttps://doi.org/10.1109/csci.2014.150
dc.identifier.doi10.1109/csci.2014.150
dc.identifier.endpage314
dc.identifier.isbn978-1-4799-3009-8
dc.identifier.startpage313
dc.identifier.urihttps://hdl.handle.net/20.500.14981/54188
dc.identifier.wos000355910900067
dc.language.isoeng
dc.publisherIEEE
dc.relation.conferenceInternational Conference on Computational Science and Computational Intelligence (CSCI)
dc.relation.ispartof2014 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI), VOL 2
dc.subjectComputer aided design (CAD)
dc.subjectneural networks
dc.subjectpositive second generation current conveyor (CCII plus )
dc.subjectmultilayer perceptron (MLP)
dc.subjectComputer Science
dc.titleA Neural Network-Based Design Automation of a Second Generation Current Conveyor
dc.typeProceedings Paper
dspace.entity.typePublication
local.import.sourceWOS

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