Exponential synchronization for delayed recurrent neural networks via periodically intermittent control

• Published in: Neurocomputing (Amsterdam) Vol. 113; pp. 122 - 129
• Main Authors: Xing, Jiuju, Jiang, Haijun, Hu, Cheng
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 03.08.2013; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Connectionism. Neural networks; Exact sciences and technology; Exponential synchronization; Nonlinear dynamics and nonlinear dynamical systems; Periodically intermittent control; Physics; Recurrent neural network; Statistical physics, thermodynamics, and nonlinear dynamical systems; Synchronization ; coupled oscillators; Time-varying delay; Recurrent neural network; Time-varying delay; Exponential synchronization; Periodically intermittent control; Recurrent neural nets; Delay systems; Induction; Synchronization; Time-varying systems; Intermittency; Functional theory; Efficiency; Neural networks; Time delay; Lyapunov function; Lyapunov methods
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2013.01.041

In this paper, the exponential synchronization for a class of delayed recurrent neural networks is investigated by virtue of intermittent control schemes. Based on p-norm and ∞-norm, respectively, several new and useful synchronization criteria are derived by applying Lyapunov functional theory, mathematical induction and inequality technique. Particularly, some feasible regions of control parameters for each neuron are derived for the realization of exponential synchronization, which provides great convenience to the applications of the theoretical results. Finally, some numerical simulations are given to demonstrate the effectiveness of the proposed control methods.