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A sufficient condition for absolute stability of a larger class of dynamical neural networks

dc.contributor.authorTavsanoglu, V
dc.contributor.authorArik, S
dc.date.accessioned2021-03-06T10:50:18Z
dc.date.available2021-03-06T10:50:18Z
dc.date.issued2000
dc.identifier.citationArik S., Tavsanoglu V., "A sufficient condition for absolute stability of a larger class of dynamical neural networks", IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS, cilt.47, ss.758-760, 2000
dc.identifier.issn1057-7122
dc.identifier.otherav_ec45e733-1f9b-473d-ae4d-94115df432a4
dc.identifier.othervv_1032021
dc.identifier.urihttp://hdl.handle.net/20.500.12627/155134
dc.identifier.urihttps://doi.org/10.1109/81.847881
dc.description.abstractIn this paper, we present a sufficient condition for absolute stability of a larger class of dynamical neural networks. It is shown that the H-matrix condition on the interconnection matrix ensures the existence, uniqueness and global asymptotic stability (GAS) of the equilibrium point with respect to slope-limited activation functions.
dc.language.isoeng
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectSinyal İşleme
dc.subjectMÜHENDİSLİK, ELEKTRİK VE ELEKTRONİK
dc.subjectMühendislik
dc.subjectBilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği
dc.subjectMühendislik ve Teknoloji
dc.titleA sufficient condition for absolute stability of a larger class of dynamical neural networks
dc.typeMakale
dc.relation.journalIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS
dc.contributor.department, ,
dc.identifier.volume47
dc.identifier.issue5
dc.identifier.startpage758
dc.identifier.endpage760
dc.contributor.firstauthorID57497


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