Regular Papers
International Journal of Control, Automation and Systems 2019; 17(10): 2666-2676
Published online July 4, 2019
https://doi.org/10.1007/s12555-018-0936-6
© The International Journal of Control, Automation, and Systems
Robust Stabilization of Memristor-based Coupled Neural Networks with Time-varying Delays
The robust stabilization problem of memristor-based coupled neural networks (MNNs) is addressed in this paper. Firstly, the fuzzy model of MNNs is obtained by considering the properties of memristor and corresponding circuit, some predictable assumptions on the boundedness and Lipschitz continuity of activation functions are formulated. Secondly, based on T-S fuzzy theory and Lyapunov-Krasovskii functional method, robust stabilization criteria are derived in form of linear matrix inequalities (LMIs). Finally a numerical example is presented to demonstrate the effectiveness of the proposed robust stabilization criteria, which well supports theoretical results.
Keywords Coupled neural networks, memristor, robust stabilization, T-S fuzzy, time-varying delays.
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Regular Papers
International Journal of Control, Automation and Systems 2019; 17(10): 2666-2676
Published online October 1, 2019 https://doi.org/10.1007/s12555-018-0936-6
Copyright © The International Journal of Control, Automation, and Systems.
Robust Stabilization of Memristor-based Coupled Neural Networks with Time-varying Delays
Qianhua Fu*, Jingye Cai, and Shouming Zhong
Xihua University
Abstract
The robust stabilization problem of memristor-based coupled neural networks (MNNs) is addressed in this paper. Firstly, the fuzzy model of MNNs is obtained by considering the properties of memristor and corresponding circuit, some predictable assumptions on the boundedness and Lipschitz continuity of activation functions are formulated. Secondly, based on T-S fuzzy theory and Lyapunov-Krasovskii functional method, robust stabilization criteria are derived in form of linear matrix inequalities (LMIs). Finally a numerical example is presented to demonstrate the effectiveness of the proposed robust stabilization criteria, which well supports theoretical results.
Keywords: Coupled neural networks, memristor, robust stabilization, T-S fuzzy, time-varying delays.
Vol. 22, No. 12, pp. 3545~3811
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