An analog implementation of radial basis neural networks (RBNN) using BiCMOS technology

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-27T11:20:20Z
dc.date.available2014-05-27T11:20:20Z
dc.date.created2014-05-27T11:20:20Z
dc.date.issued2001-12-01
dc.identifierMidwest Symposium on Circuits and Systems, v. 2, p. 705-708.
dc.identifierhttp://hdl.handle.net/11449/66666
dc.identifier10.1109/MWSCAS.2001.986285
dc.identifierWOS:000175971700158
dc.identifier2-s2.0-0035575292
dc.identifier1525717947689076
dc.description.abstractThis paper describes a analog implementation of radial basis neural networks (RBNN) in BiCMOS technology. The RBNN uses a gaussian function obtained through the characteristic of the bipolar differential pair. The gaussian parameters (gain, center and width) is changed with programmable current source. Results obtained with PSPICE software is showed.
dc.languageeng
dc.relationMidwest Symposium on Circuits and Systems
dc.rightsAcesso aberto
dc.sourceScopus
dc.subjectCMOS integrated circuits
dc.subjectComputer software
dc.subjectElectric currents
dc.subjectGain measurement
dc.subjectNeural networks
dc.subjectNumerical methods
dc.subjectVLSI circuits
dc.subjectBiCMOS technology
dc.subjectGaussian function
dc.subjectProgrammable current source
dc.subjectRadial basis neural networks
dc.subjectIntegrated circuit manufacture
dc.titleAn analog implementation of radial basis neural networks (RBNN) using BiCMOS technology
dc.typeActas de congresos


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