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

arXiv: https://arxiv.org/abs/1804.08440 · HTML: https://mlsanity.com/paper/1804.08440/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 1 of 1)

- [Exponential convergence rates for momentum stochastic gradient descent in the overparametrized setting](https://mlsanity.com/paper/2302.03550/) (2023)

## Abstract (arXiv)

The 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. A new Gronwall type results are used to estimate the settling time. An example of a neural network which is finite-time stable is given