Global exponential dissipativity and stabilization of memristor-based recurrent neural networks with time-varying delays.
This paper addresses the global exponential dissipativity of memristor-based recurrent neural networks with time-varying delays. By constructing proper Lyapunov functionals and using M-matrix theory and LaSalle invariant principle, the sets of global exponentially dissipativity are characterized parametrically. It is proven herein that there are 2(2n(2)-n) equilibria for an n-neuron memristor-based neural network and they are located in the derived globally attractive sets. It is also shown that memristor-based recurrent neural networks with time-varying delays are stabilizable at the origin of the state space by using a linear state feedback control law with appropriate gains. Finally, two numerical examples are discussed in detail to illustrate the characteristics of the results.
College of Mathematics and Econometrics, Hunan University, Changsha 410082, China; Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong. Electronic address: zguo@hnu.edu.cn.
Zhenyuan
Z
Guo
Jun
J
Wang
Zheng
Z
Yan
D000465:Algorithms; D016014:Linear Models; D016571:Neural Networks, Computer; D009474:Neurons; D017711:Nonlinear Dynamics
Global exponential dissipativity; Memristor; Neurodynamics; Recurrent neural networks; Stabilization