Event-Based Dissipative Analysis for Discrete Time-Delay Singular Jump Neural Networks

• Published in: IEEE transaction on neural networks and learning systems Vol. 31; no. 4; pp. 1232 - 1241
• Main Authors: Zhang, Yingqi, Shi, Peng, Agarwal, Ramesh K., Shi, Yan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Computational modeling; Control systems; Delays; Dissipativity; event-based communication technique; Markovian jump parameters; Neural networks; singular neural networks; Stability analysis; Symmetric matrices; time-varying delays
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2019.2919585

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.