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Global exponential stability for switched memristive neural networks with time-varying delays

Research paper by Youming Xin, Yuxia Li, Zunshui Cheng, Xia Huang

Indexed on: 21 Apr '16Published on: 20 Apr '16Published in: Neural Networks



Abstract

This paper considers the problem of exponential stability for switched memristive neural networks (MNNs) with time-varying delays. Different from most of the existing papers, we model a memristor as a continuous system, and view switched MNNs as switched neural networks with uncertain time-varying parameters. Based on average dwell time technique, mode-dependent average dwell time technique and multiple Lyapunov-Krasovskii functional approach, two conditions are derived to design the switching signal and guarantee the exponential stability of the considered neural networks, which are delay-dependent and formulated by linear matrix inequalities (LMIs). Finally, the effectiveness of the theoretical results is demonstrated by two numerical examples.

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