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Article: On impulsive autoassociative neural networks

TitleOn impulsive autoassociative neural networks
Authors
Issue Date2000
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/neunet
Citation
Neural Networks, 2000, v. 13 n. 1, p. 63-69 How to Cite?
AbstractMany systems existing in physics, chemistry, biology, engineering, and information science can be characterized by impulsive dynamics caused by abrupt jumps at certain instants during the process. These complex dynamical behaviors can be modeled by impulsive differential systems or impulsive neural networks. This paper formulates and studies a new model of impulsive autoassociative neural networks. Several fundamental issues, such as global exponential stability and existence and uniqueness of equilibria of such neural networks, are established. Copyright (C) 2000 Elsevier Science Ltd. | Many systems existing in physics, chemistry, biology, engineering, and information science can be characterized by impulsive dynamics caused by abrupt jumps at certain instants during the process. These complex dynamical behaviors can be modeled by impulsive differential systems or impulsive neural networks. This paper formulates and studies a new model of impulsive autoassociative neural networks. Several fundamental issues, such as global exponential stability and existence and uniqueness of equilibria of such neural networks, are established.
Persistent Identifierhttp://hdl.handle.net/10722/156547
ISSN
2015 Impact Factor: 3.216
2015 SCImago Journal Rankings: 1.629
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorGuan, ZHen_US
dc.contributor.authorLam, Jen_US
dc.contributor.authorChen, Gen_US
dc.date.accessioned2012-08-08T08:42:54Z-
dc.date.available2012-08-08T08:42:54Z-
dc.date.issued2000en_US
dc.identifier.citationNeural Networks, 2000, v. 13 n. 1, p. 63-69en_US
dc.identifier.issn0893-6080en_US
dc.identifier.urihttp://hdl.handle.net/10722/156547-
dc.description.abstractMany systems existing in physics, chemistry, biology, engineering, and information science can be characterized by impulsive dynamics caused by abrupt jumps at certain instants during the process. These complex dynamical behaviors can be modeled by impulsive differential systems or impulsive neural networks. This paper formulates and studies a new model of impulsive autoassociative neural networks. Several fundamental issues, such as global exponential stability and existence and uniqueness of equilibria of such neural networks, are established. Copyright (C) 2000 Elsevier Science Ltd. | Many systems existing in physics, chemistry, biology, engineering, and information science can be characterized by impulsive dynamics caused by abrupt jumps at certain instants during the process. These complex dynamical behaviors can be modeled by impulsive differential systems or impulsive neural networks. This paper formulates and studies a new model of impulsive autoassociative neural networks. Several fundamental issues, such as global exponential stability and existence and uniqueness of equilibria of such neural networks, are established.en_US
dc.languageengen_US
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/neuneten_US
dc.relation.ispartofNeural Networksen_US
dc.subject.meshModels, Neurologicalen_US
dc.subject.meshNeural Networks (Computer)en_US
dc.titleOn impulsive autoassociative neural networksen_US
dc.typeArticleen_US
dc.identifier.emailLam, J:james.lam@hku.hken_US
dc.identifier.authorityLam, J=rp00133en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/S0893-6080(99)00095-7en_US
dc.identifier.pmid10935460-
dc.identifier.scopuseid_2-s2.0-0033967086en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0033967086&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume13en_US
dc.identifier.issue1en_US
dc.identifier.spage63en_US
dc.identifier.epage69en_US
dc.identifier.isiWOS:000085549200010-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridGuan, ZH=7202542255en_US
dc.identifier.scopusauthoridLam, J=7201973414en_US
dc.identifier.scopusauthoridChen, G=36012928800en_US

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