Dynamical and Static Multisynchronization of Coupled Multistable Neural Networks via Impulsive Control

• Published in: IEEE transaction on neural networks and learning systems Vol. 29; no. 12; pp. 6062 - 6072
• Main Authors: Lv, XiaoXiao, Li, Xiaodi, Cao, Jinde, Perc, Matjaz
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Average impulsive interval; Biological neural networks; Computer Simulation; Control systems design; Control theory; coupled multistable neural networks (CMNNs); Delays; Humans; impulsive control; Liapunov functions; Linear matrix inequalities; linear matrix inequality (LMI); Mathematical analysis; Matrix methods; multisynchronization; Neural networks; Neural Networks (Computer); Nonlinear Dynamics; Reaction Time; Switches; Switching theory; Symmetric matrices; Synchronization; Time Factors; Topology
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2018.2816924

This paper investigates the dynamical multisynchronization and static multisynchronization problem for delayed coupled multistable neural networks with fixed and switching topologies. To begin with, a class of activation functions as well as several sufficient conditions are introduced to ensure that every subnetwork has multiple equilibrium states. By constructing an appropriate Lyapunov function and by employing impulsive control theory and the average impulsive interval method, several sufficient conditions for multisynchronization in terms of linear matrix inequalities (LMIs) are obtained. Moreover, a unified impulsive controller is designed by means of the established LMIs. Finally, a numerical example is presented to demonstrate the effectiveness of the presented impulsive control strategy.