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https://hdl.handle.net/2440/132644
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Type: | Journal article |
Title: | Event-based dissipative analysis for discrete time-delay singular jump neural networks |
Author: | Zhang, Y. Shi, P. Agarwal, R.K. Shi, Y. |
Citation: | IEEE Transactions on Neural Networks and Learning Systems, 2020; 31(4):1232-1241 |
Publisher: | IEEE |
Issue Date: | 2020 |
ISSN: | 2162-237X 2162-2388 |
Statement of Responsibility: | Yingqi Zhang; Peng Shi; Ramesh K. Agarwal; Yan Shi |
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. |
Keywords: | Dissipativity; event-based communication technique; Markovian jump parameters; singular neural networks; time-varying delays |
Rights: | © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. |
DOI: | 10.1109/TNNLS.2019.2919585 |
Grant ID: | http://purl.org/au-research/grants/arc/DP170102644 |
Published version: | http://dx.doi.org/10.1109/tnnls.2019.2919585 |
Appears in Collections: | Electrical and Electronic Engineering publications |
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