Adaptive event-triggered H∞ filtering for discrete-time delayed neural networks with randomly occurring missing measurements

The problem of adaptive event-triggered H∞ filtering for a class of discrete-time delayed neural networks with random occurring missing measurements is investigated in this paper. The random missing measurements are described by a Bernoulli distributed white sequence, which obeys a conditional probability distribution. In order to avoid the unnecessary waste of the limited communication resources, a novel event-triggered scheme with an adaptive triggering parameter is introduced to determine whether or not the current measurements should be sent out. The delay-dependent sufficient conditions have been derived to guarantee the asymptotical stability of the augmented filtering system and achieve the prescribed H∞ disturbance attenuation level. The design method of the H∞ filter is also presented. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.