Finite-time stability for differential inclusions with applications to neural networks.

This paper investigates sufficient conditions on a differential inclusion which guarantee that the origin is a finite-time stable equilibrium, namely a weak local one, a weak global one, or a strong local one. The analysis relies on the existence of a Lyapunov function. New Gronwall-type results are used to estimate the settling time. An example of a neural network which is finite-time stable is given.