Global robust exponential stability of discrete-time interval BAM neural networks with time-varying delays

This paper is concerned with global robust exponential stability for a class of discrete-time interval bidirectional associative memory (BAM) neural networks with time-varying delays. By employing the Lyapunov functional and linear matrix inequality (LMI) approach, a new sufficient criterion is proposed for the global robust exponential stability of discrete-time BAM neural networks which contain uncertain parameters with their values being bounded. The proposed LMI-based results are computationally efficient as they can be easily checked via the LMI toolbox. Finally, two examples are provided to demonstrate the effectiveness of the obtained results.