Adaptive event triggered H filtering for discrete time delayed neural networks with randomly occurring missing measurements
Abstract 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.
Signal Process.