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Global asymptotic stability of complex-valued neural networks with additive time-varying delays

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Abstract

In this paper, we extensively study the global asymptotic stability problem of complex-valued neural networks with leakage delay and additive time-varying delays. By constructing a suitable Lyapunov–Krasovskii functional and applying newly developed complex valued integral inequalities, sufficient conditions for the global asymptotic stability of proposed neural networks are established in the form of complex-valued linear matrix inequalities. This linear matrix inequalities are efficiently solved by using standard available numerical packages. Finally, three numerical examples are given to demonstrate the effectiveness of the theoretical results.

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Acknowledgements

The authors wish to thank the editor and reviewers for a number of constructive comments and suggestions that have improved the quality of this manuscript. This work was supported by Science Engineering Research Board (SERB), DST, Govt. of India under YSS Project F.No: YSS/2014/000447 dated 20.11.2015.

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Authors and Affiliations

  1. Department of Mathematics, The Gandhigram Rural Institute - Deemed University, Gandhigram, Tamilnadu, 624 302, India

    K. Subramanian & P. Muthukumar

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  1. K. Subramanian
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  2. P. Muthukumar
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Correspondence to P. Muthukumar.

Additional information

This work was supported by Science Engineering Research Board (SERB), DST, Govt. of India under YSS Project F. No: YSS/2014/000447 dated 20. 11. 2015.

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Subramanian, K., Muthukumar, P. Global asymptotic stability of complex-valued neural networks with additive time-varying delays. Cogn Neurodyn 11, 293–306 (2017). https://doi.org/10.1007/s11571-017-9429-1

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