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Stability of Analytic Neural Networks With Event-Triggered Synaptic Feedbacks
KTH, School of Electrical Engineering (EES), Automatic Control. Fudan University, China.
2016 (English)In: IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, E-ISSN 2162-2388, Vol. 27, no 2, 483-494 p.Article in journal (Refereed) Published
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Abstract [en]

In this paper, we investigate stability of a class of analytic neural networks with the synaptic feedback via event-triggered rules. This model is general and include Hopfield neural network as a special case. These event-trigger rules can efficiently reduces loads of computation and information transmission at synapses of the neurons. The synaptic feedback of each neuron keeps a constant value based on the outputs of the other neurons at its latest triggering time but changes at its next triggering time, which is determined by a certain criterion. It is proved that every trajectory of the analytic neural network converges to certain equilibrium under this event-triggered rule for all the initial values except a set of zero measure. The main technique of the proof is the Lojasiewicz inequality to prove the finiteness of trajectory length. The realization of this event-triggered rule is verified by the exclusion of Zeno behaviors. Numerical examples are provided to illustrate the efficiency of the theoretical results.

Place, publisher, year, edition, pages
IEEE , 2016. Vol. 27, no 2, 483-494 p.
Keyword [en]
Almost stability, analytic neural network, event-triggered rule, Zenoa behaviors
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-185081DOI: 10.1109/TNNLS.2015.2488903ISI: 000372020500024Scopus ID: 2-s2.0-84945895103OAI: oai:DiVA.org:kth-185081DiVA: diva2:919723
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QC 20160414

Available from: 2016-04-14 Created: 2016-04-11 Last updated: 2016-04-14Bibliographically approved

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