title: An improved algebraic criterion for global exponential stability of recurrent neural networks with time-varying delays.
abstract: 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.
authors.affiliation: Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China. yishen64@163.com
authors.forename: Yi
authors.initials: Y
authors.lastname: Shen
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D013997:Time Factors