RAZUMIKHIN-TYPE THEOREMS ON STABILITY OF STOCHASTIC NEURAL NETWORKS WITH DELAYS
S. Blythe, Xuerong Mao, Anita Shah · Stochastic Analysis and Applications · 2001
Although the stability of neural networks has been studied by many authors, the problem of stochastic effects on the stability has not been investigated until recently by Liao and Mao (Stochastic Anal. and Appl. 1996, 14, 165–185; Neural Parallel and Sci. Computations 1996, 4, 205–224). In this paper, we shall investigate the stability problem for stochastic neural networks with time-varying delay. The main technique employed in this paper is the well-known Razumikhin argument, which is completely different from those used in Liao and Mao.