Zhang, Y.Shi, P.Agarwal, R.K.Shi, Y.2021-10-152021-10-152020IEEE Transactions on Neural Networks and Learning Systems, 2020; 31(4):1232-12412162-237X2162-2388https://hdl.handle.net/2440/132644This 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 (X ι , Y ι , Z ι , 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 (X ι , Y ι , Z ι , δ). An example is also given to illustrate the effectiveness of the proposed method.en© 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.Dissipativity; event-based communication technique; Markovian jump parameters; singular neural networks; time-varying delaysJournal article10.1109/TNNLS.2019.29195852021-10-15529147Shi, P. [0000-0001-6295-0405] [0000-0001-8218-586X] [0000-0002-0864-552X] [0000-0002-1358-2367] [0000-0002-5312-5435]