Study of Asymptotical Stability of Transiently Chaotic Neural Networks

The asymptotic stability of transiently chaotic neural networks is mainly studied in synchronously updating mode, and some results on the asymptotic stability of the networks are obtained by defining an energy function and taking some inequality techniques into account, where the connection matrix of the networks is asymmetric. In this paper, several sufficient conditions which guarantee that the networks can asymptotically converge to a stable fixed point are presented. The results given here improve and generalize some existing results in the previous references.