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On the identification of recurrent neural nets

descriptionPublicationkeyboard_double_arrow_right Article , Conference object 11 Nov 2002Publisher:IEEEJournal:Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), volume 2, pages 1,991-1,995
Authors: Dietmar Trummer; Manfred Deistler;

doi: 10.1109/cdc.2000.912156

On the identification of recurrent neural nets

Abstract

Observational equivalence for so-called Jordan networks, which are a special class of recurrent networks, is analysed. We show this type of neural nets to belong to a wider class of mixed networks and use the description of observational equivalence available for the latter class for obtaining the respective results for the first class.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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