An improved algebraic criterion for global exponential stability of recurrent neural networks with time-varying delays.
This brief paper presents an M-matrix-based algebraic criterion for the global exponential stability of a class of recurrent neural networks with decreasing time-varying delays. The criterion improves some previous criteria based on M-matrix and is easy to be verified with the connection weights of the recurrent neural networks with decreasing time-varying delays. In addition, the rate of exponential convergence can be estimated via a simple computation based on the criterion herein.
Shen
Yi
Y
Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China. yishen64@163.com
Wang
Jun
J
D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D013997:Time Factors