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H_∞ Stability of Neural Networks Switched at An Arbitrary Time

Choon Ki Ahn

Abstract


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.

Keywords


H_∞ stability, switched neural networks, linear matrix inequality (LMI)

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