| On the global output convergence of a class of recurrent neural networks with time-varying inputs. | |
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MedLine Citation:
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PMID: 15795114 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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This paper studies the global output convergence of a class of recurrent neural networks with globally Lipschitz continuous and monotone nondecreasing activation functions and locally Lipschitz continuous time-varying inputs. We establish two sufficient conditions for global output convergence of this class of neural networks. Symmetry in the connection weight matrix is not required in the present results which extend the existing ones. |
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Authors:
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Sanqing Hu; Derong Liu |
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Publication Detail:
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Type: Comparative Study; Journal Article; Research Support, U.S. Gov't, Non-P.H.S. Date: 2005-01-19 |
Journal Detail:
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Title: Neural networks : the official journal of the International Neural Network Society Volume: 18 ISSN: 0893-6080 ISO Abbreviation: Neural Netw Publication Date: 2005 Mar |
Date Detail:
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Created Date: 2005-03-29 Completed Date: 2005-06-13 Revised Date: 2006-11-15 |
Medline Journal Info:
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Nlm Unique ID: 8805018 Medline TA: Neural Netw Country: United States |
Other Details:
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Languages: eng Pagination: 171-8 Citation Subset: IM |
Affiliation:
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Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Computer Simulation Feedback Humans Linear Models Models, Neurological* Neural Networks (Computer)* Neurons / physiology* Reaction Time / physiology Signal Processing, Computer-Assisted |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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