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

This paper focuses on the robust exponential convergence of uncertain Takagi-Sugeno (T-S) fuzzy bidirectional associative memory (BAM) neural networks with time-varying delays. Using Lyapunov method and delay inequality technique, several sufficiently easily verifiable criteria are derived to guarantee that the T-S fuzzy BAM neural networks with time-varying delays to converge robustly exponentially to a ball in the state space with a pre-specified rate. Finally, a numerical example with simulations is given to illustrate the effectiveness of our theoretical results.