Event-Based Dissipative Analysis for Discrete Time-Delay Singular Jump Neural Networks
Yingqi Zhang, Peng Jia Shi, Ramesh K. Agarwal, Yan Shi · IEEE Transactions on Neural Networks and Learning Systems · 2019
This paper investigates the event-triggered dissipative filtering issue for discrete-time singular neural networks with time-varying delays and Markovian jump parameters. Via event-triggered communication technique, a singular jump neural network (SJNN) model of network-induced delays is first given, and sufficient criteria are then provided to guarantee that the resulting augmented SJNN is stochastically admissible and strictly stochastically dissipative (SASSD) with respect to (Xι, Yι, Zι, δ) by using slack matrix scheme. Furthermore, employing filter equivalent technique, codesigned filter gains, and event-triggered matrices are derived to make sure that the augmented SJNN model is SASSD with respect to (Xι, Yι, Zι, δ). An example is also given to illustrate the effectiveness of the proposed method.