title: Stability of Analytic Neural Networks With Event-Triggered Synaptic Feedbacks.
abstract: 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 Łojasiewicz 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.
authors.forename: Ren
authors.initials: R
authors.lastname: Zheng
authors.forename: Xinlei
authors.initials: X
authors.lastname: Yi
authors.forename: Wenlian
authors.initials: W
authors.lastname: Lu
authors.forename: Tianping
authors.initials: T
authors.lastname: Chen