| Summary: | The problem of event-triggered $ H_\infty $ filtering for discrete-time Markov jump delayed neural networks with quantizations is investigated in this paper. Firstly, an event-triggered communication scheme is proposed to determine whether or not the current sampled data can be transmitted to the quantizer. Secondly, a quantizer is used to quantify the sampled data, which can reduce the data transmission rate in the network. Next, through the analysis of network-induced delay's intervals, the discrete-time neural network, the event-triggered scheme and network-induced delay are unified into a discrete-time Markov jump delayed neural network. As a result, the sufficient conditions are obtained to guarantee the stability and $ H_\infty $ performance of the augmented system and to present the $ H_\infty $ filter design. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method.
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