Title :
Global exponential stability and periodicity of recurrent neural networks with time delays
Author :
Cao, Jinde ; Wang, Jun
Author_Institution :
Dept. of Math., Southeast Univ., Nanjing, China
fDate :
5/1/2005 12:00:00 AM
Abstract :
In this paper, the global exponential stability and periodicity of a class of recurrent neural networks with time delays are addressed by using Lyapunov functional method and inequality techniques. The delayed neural network includes the well-known Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks as its special cases. New criteria are found to ascertain the global exponential stability and periodicity of the recurrent neural networks with time delays, and are also shown to be different from and improve upon existing ones.
Keywords :
Hopfield neural nets; Lyapunov methods; asymptotic stability; delays; Hopfield neural networks; Lyapunov functional method; bidirectional associative memory networks; cellular neural networks; global exponential stability; inequality techniques; recurrent neural networks; time delays; Associative memory; Automation; Cellular neural networks; Delay effects; Hopfield neural networks; Lyapunov method; Neural networks; Recurrent neural networks; Stability analysis; Stability criteria; Inequality; Lyapunov method; periodicity; recurrent neural networks; stability; time delay;
Journal_Title :
Circuits and Systems I: Regular Papers, IEEE Transactions on
DOI :
10.1109/TCSI.2005.846211