Event-based dissipative analysis for discrete time-delay singular jump neural networks
Date
2020
Authors
Zhang, Y.
Shi, P.
Agarwal, R.K.
Shi, Y.
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Journal article
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IEEE Transactions on Neural Networks and Learning Systems, 2020; 31(4):1232-1241
Statement of Responsibility
Yingqi Zhang; Peng Shi; Ramesh K. Agarwal; Yan Shi
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Abstract
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 (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.
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© 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.