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- W2267501856 abstract "In this paper, we investigate convergence of a class of analytic neural networks with event-triggered rule. This model is general and include Hopfield neural network as a special case. The event-trigger rule efficiently reduces the frequency of information transmission between synapses of the neurons. The synaptic feedback of each neuron keeps a constant value based on the outputs of its neighbours at its latest triggering time but changes until the next triggering time of this neuron that is determined by certain criterion via its neighborhood information. It is proved that the analytic neural network is completely stable under this event-triggered rule. The main technique of proof is the ${L}$ojasiewicz 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 theoretical results and present the optimisation capability of the network dynamics." @default.
- W2267501856 created "2016-06-24" @default.
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- W2267501856 date "2015-04-30" @default.
- W2267501856 modified "2023-09-26" @default.
- W2267501856 title "Global Convergence of Analytic Neural Networks with Event-triggered Synaptic Feedbacks" @default.
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- W2267501856 doi "https://doi.org/10.48550/arxiv.1504.08081" @default.
- W2267501856 hasPublicationYear "2015" @default.
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