LAUSR

This brief is concerned with the issue of dissipativity analysis for delayed recurrent neural networks. First, a flexible negative-definiteness determination method is presented, which brings more flexibility and can further reduce the conservatism of some existing methods. Second, by employing the flexible negative-definiteness determination method and some integral inequalities, a tight upper bound of the Lyapunov-Krasovkii functional derivative can be derived. Then, a less conservative delay-dependent criterion is derived to guarantee delayed recurrent neural networks strictly dissipative. Finally, simulations are provided to confirm the superiority of our result.