H∞ stability of neural networks switched at an arbitrary time

This article proposes a novel approach to stability analysis of neural networks switched at an arbitrary time. First, a new condition for H∞ stability of switched neural networks is proposed. Second, a new H∞ stability condition in the form of linear matrix inequality (LMI) for these neural networks is proposed. These conditions ensure to reduce the H∞ norm from the external input to the state vector within a disturbance attenuation level. Without the external input, the proposed conditions also guarantee asymptotic stability.