2015/04/30 by Wenlian Lu, Ren Zheng, Lu, Wenlian +5
Computer Science · Engineering · #Adaptation and Self-Organizing Systems (nlin.AO) #Advanced Memory and Neural Computing #FOS: Mathematics #FOS: Physical sciences #Neural Networks Stability and Synchronization #Neural Networks and Applications #Optimization and Control (math.OC)
paper · pdf · doi:10.48550/arxiv.1504.08081
openalex publication_date 2015/04/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 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 theoretical results and present the optimisation capability of the network dynamics.