Title :
An Improved Algebraic Criterion for Global Exponential Stability of Recurrent Neural Networks With Time-Varying Delays
Author :
Shen, Yi ; Wang, Jun
Author_Institution :
Huazhong Univ. of Sci. & Technol., Wuhan
fDate :
3/1/2008 12:00:00 AM
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.
Keywords :
asymptotic stability; matrix algebra; recurrent neural nets; M-matrix-based algebraic criterion; exponential convergence; global exponential stability; recurrent neural networks; time-varying delays; $M$-matrix; Global exponential stability; recurrent neural networks; time-varying delays; Algorithms; Computer Simulation; Humans; Neural Networks (Computer); Nonlinear Dynamics; Pattern Recognition, Automated; Time Factors;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.911751
