Matrix measure method for global exponential stability of complex-valued recurrent neural networks with time-varying delays.
In this paper, based on the matrix measure method and the Halanay inequality, global exponential stability problem is investigated for the complex-valued recurrent neural networks with time-varying delays. Without constructing any Lyapunov functions, several sufficient criteria are obtained to ascertain the global exponential stability of the addressed complex-valued neural networks under different activation functions. Here, the activation functions are no longer assumed to be derivative which is always demanded in relating references. In addition, the obtained results are easy to be verified and implemented in practice. Finally, two examples are given to illustrate the effectiveness of the obtained results.
Department of Mathematics, Southeast University, Nanjing 210096, China.
Weiqiang
W
Gong
Department of Mathematics, Southeast University, Nanjing 210096, China; CSN Research Group, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia. Electronic address: jinlliang@seu.edu.cn.
Jinling
J
Liang
Department of Mathematics, Southeast University, Nanjing 210096, China; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Jinde
J
Cao
D000465:Algorithms; D015233:Models, Statistical; D016571:Neural Networks, Computer
Complex-valued recurrent neural networks; Exponential stability; Halanay inequality; Matrix measure; Time-varying delay