document: Asymptotical stability in discrete-time neural networks In this work , we present a proof of the existence of a fixed point and a generalized sufficient condition that guarantees the stability of it in discrete-time neural networks by using the Lyapunov function method . We also show that for both symmetric and asymmetric connections , the unique attractor is a fixed point when several conditions are satisfied . This is an extended result of Chen and Aihara -LRB- see Physica D , vol . 104 , no. 3/4 , p. 286-325 , 1997 -RRB- . In particular , we further study the stability of equilibrium in discrete-time neural networks with the connection weight matrix in form of an interval matrix . Finally , several examples are shown to illustrate and reinforce our theory
doc_bio_tags: B I O O O O O O O O O O O O O O O O O B I O O B I I O O O B O O O O O O O O O B I I O O O O O O O O O B I O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B I I O O O O B I O O O O O O O O O O O O O
extractive_keyphrases: asymptotical stability fixed point lyapunov function method asymmetric connections unique attractor stability connection weight matrix interval matrix generalized sufficient condition
abstractive_keyphrases: symmetric connections equilibrium stability discrete-time neural networks