Robust asymptotic stability of uncertain fuzzy BAM neural networks with time-varying delays

Via ordinary Takagi–Sugeno (TS) fuzzy models, complex nonlinear systems can be represented to a set of linear sub-models by using fuzzy sets and fuzzy reasoning. In this paper, the global asymptotic stability problem of TS fuzzy bi-directional associative memories (BAM) neural networks with time-varying delays and parameter uncertainties is considered. First, the model of TS fuzzy BAM neural networks with time-varying delays and parameter uncertainties is established as a modified TS fuzzy model in which the consequent parts are composed of a set of BAM neural networks with time-varying delays. Secondly, the globally robust asymptotically stable condition is presented in terms of linear matrix inequalities, which can be easily solved by some standard numerical packages. Two numerical examples are also given to validate the theoretical results.