LMI-based finite-time boundedness analysis of neural networks with parametric uncertainties

In this paper, the problem of finite-time boundedness (FTB) for neural networks with parameter uncertainties is studied. We extend the concept of FTB for neural networks. Based on the linear matrix inequality (LMI) technique, a sufficient condition is derived to guarantee FTB for uncertain neural networks. Besides, we also simply give a sufficient condition for certain systems. Finally, illustrative examples are given to demonstrate the validity of the proposed theoretical results.