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The stabilization of BAM neural networks with time-varying delays in the leakage terms via sampled-data control

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Abstract

This paper is concerned with the stabilization of bidirectional associative memory neural networks with time-varying delays in the leakage terms using sampled-data control. We apply an input delay approach to change the sampling system into a continuous time-delay system. Based on the Lyapunov theory, some stability criteria are obtained. These conditions are expressed in terms of linear matrix inequalities and can be solved via standard numerical software. Finally, one numerical example is given to demonstrate the effectiveness of the proposed results .

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Acknowledgments

This work was jointly supported by the National Natural Science Foundation of China under Grant 11226116, the Foundation of Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University) Ministry of Education, P.R. China, and the Fundamental Research Funds for the Central Universities (JUSRP51317B, JUDCF13042).

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Authors and Affiliations

  1. School of Science, Jiangnan University, Wuxi, People’s Republic of China

    Li Li & Yongqing Yang

  2. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Wuxi, 214122, People’s Republic of China

    Li Li

  3. Department of Mathematics, School of Mechanical Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Guang Lin

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  1. Li Li
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  2. Yongqing Yang
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  3. Guang Lin
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Correspondence to Li Li.

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Li, L., Yang, Y. & Lin, G. The stabilization of BAM neural networks with time-varying delays in the leakage terms via sampled-data control. Neural Comput & Applic 27, 447–457 (2016). https://doi.org/10.1007/s00521-015-1865-4

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  • DOI: https://doi.org/10.1007/s00521-015-1865-4

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