H∞ output synchronization of directed coupled reaction-diffusion neural networks via event-triggered quantized control

Abstract By designing a quantized controller based on event trigger, this paper considers the problem of H ∞ output synchronization for coupled neural networks with reaction-diffusion term and directed topology. Firstly, in this hybrid control strategy, the data is sampled in time domain to exclude the Zeno-behavior before judging whether an event is triggered, and then the event-triggered data instead of the sampling data itself is quantized by a logarithmic quantizer. Secondly, some sufficient conditions for H ∞ output synchronization are obtained, in which the dimension of these conditions can be reduced to only depend on the number of neurons, but not on the number of nodes. Finally, a numerical example is given to verify the theoretical results.