Title :
Global Asymptotic Stability of Recurrent Neural Networks with Time Varying Delays
Author :
Guan, Huanxin ; Zhang, Huaguang ; Wang, Zhanshan ; Liu, Derong
Author_Institution :
Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang
Abstract :
In this paper, two sufficient conditions are established for the global asymptotic stability of recurrent neural networks with multiple time varying delays. The Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality approach are employed in our investigation. Our results are shown to be generalizations of some previously published results and are less conservative than existing results. The present results are also applicable to recurrent neural networks with constant time delays.
Keywords :
asymptotic stability; delays; functional equations; linear matrix inequalities; recurrent neural nets; time-varying systems; Lyapunov-Krasovskii stability theory; functional differential equations; global asymptotic stability; linear matrix inequality approach; recurrent neural networks; time varying delays; Associative memory; Asymptotic stability; Delay effects; Eigenvalues and eigenfunctions; Information science; Neural networks; Neurons; Recurrent neural networks; Sufficient conditions; Symmetric matrices;
Conference_Titel :
Circuits and Systems, 2007. ISCAS 2007. IEEE International Symposium on
Conference_Location :
New Orleans, LA
Print_ISBN :
1-4244-0920-9
Electronic_ISBN :
1-4244-0921-7
DOI :
10.1109/ISCAS.2007.378139
