Event-Triggered Exponential Synchronization for Complex-Valued Memristive Neural Networks With Time-Varying Delays

• Published in: IEEE transaction on neural networks and learning systems Vol. 31; no. 10; pp. 4104 - 4116
• Main Authors: Li, Xiaofan, Zhang, Wenbing, Fang, Jian-An, Li, Huiyuan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Asymptotic stability; Biological neural networks; Communication networks; Complex-valued memristive neural networks; Delays; event-triggered mechanism; exponential synchronization; Information exchange; Lower bounds; Memristors; Neural networks; Synchronism; Synchronization; Wireless communications; Wireless networks; Zeno behavior
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2019.2952186

This article solves the event-triggered exponential synchronization problem for a class of complex-valued memristive neural networks with time-varying delays. The drive-response complex-valued memristive neural networks are translated into two real-valued memristive neural networks through the method of separating the complex-valued memristive neural networks into real and imaginary parts. In order to reduce the information exchange frequency between the sensor and the controller, a novel event-triggered mechanism with the event-triggering functions is introduced in wireless communication networks. Some sufficient conditions are established to achieve the event-triggered exponential synchronization for drive-response complex-valued memristive neural networks with time-varying delays. In addition, to guarantee that the Zeno behavior cannot occur, a positive lower bound for the interevent times is explicitly derived. Finally, numerical simulations are provided to illustrate the effectiveness and superiority of the obtained theoretical results.