In this brief, the problem of passivity analysis is
investigated for a class of uncertain neural networks (NNs) with
both discrete and distributed time-varying delays. By constructing
a novel Lyapunov functional and utilizing some advanced
techniques, new delay-dependent passivity criteria are established
to guarantee the passivity performance of NNs. Essentially
different from the available results, when estimating the upper
bound of the derivative of Lyapunov functionals, we consider
and best utilize the additional useful terms about the distributed
delays, which leads to less conservative results. These criteria are
expressed in the form of convex optimization problems, which can
be efficiently solved via standard numerical software. Numerical
examples are provided to illustrate the effectiveness and less
conservatism of the proposed results.