Event-Based Dissipative Analysis for Discrete Time-Delay Singular Jump Neural Networks.
basic_science · Level V
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- Record sourced from PubMed, PMID 31247571.
- Also identified by DOI 10.1109/TNNLS.2019.2919585.
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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<sub>ι</sub>,Y<sub>ι</sub>,Z<sub>ι</sub>,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<sub>ι</sub>,Y<sub>ι</sub>,Z<sub>ι</sub>,δ) . An example is also given to illustrate the effectiveness of the proposed method.