Impulsive-Based Almost Surely Synchronization for Neural Network Systems Subject to Deception Attacks

• Published in: IEEE transaction on neural networks and learning systems Vol. 34; no. 5; pp. 2298 - 2307
• Main Authors: Dong, Shiyu, Zhu, Hong, Zhong, Shouming, Shi, Kaibo, Lu, Jianquan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuators; Almost surely synchronization; Artificial neural networks; Data communication; deception attacks; desynchronizing impulses; Mathematical models; neural network systems (NSSs); Neural networks; Symmetric matrices; Synchronism; Synchronization; System performance; Trajectory
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2021.3106383

This article is dedicated to investigating the impulsive-based almost surely synchronization issue of neural network systems (NSSs) with quality-of-service constraints. First, the communication network considered suffers from random double deception attacks, which are modeled as a nonlinear function and a desynchronizing impulse sequence, respectively. Meanwhile, the impulsive instants and impulsive gains are randomly and only their expectations are available. Second, by taking two different types of random deception attacks into consideration, a novel mathematical model for vulnerable NSSs is constructed. Then, almost surely synchronization criteria are established by using Borel-Cantelli lemma. Furthermore, based on the derived strong and weak sufficient conditions, the almost surely synchronization of NSSs is achieved. Finally, the section of numerical example is shown to illustrate the effectiveness of the proposed method.