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New exponential passivity of BAM neural networks with time-varying delays

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Abstract

In this paper, the exponential passivity for bidirectional associative memory (BAM) neural networks with time-varying delays is considered. In our study, the lower and upper bounds of the activation functions are allowed to be either positive, negative or zero. By constructing new and improved Lyapunov–Krasovskii functional and introducing free-weighting matrices, a new and improved delay-dependent exponential passivity criterion for BAM neural networks with time-varying delays is derived in the form of linear matrix inequality (LMI). A numerical example is given to show that the derived condition is less conservative than some existing results given in the literature.

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Acknowledgements

The first author is supported by student scholarship from the Human Resources Development in Science Project (Science Achievement Scholarship of Thailand (SAST)). The second author is supported by Chiang Mai University, Chiang Mai, Thailand.

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Authors and Affiliations

  1. Department of Mathematics, Chiang Mai University, Chiang Mai, 50200, Thailand

    J. Thipcha & P. Niamsup

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  1. J. Thipcha
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  2. P. Niamsup
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Correspondence to P. Niamsup.

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Thipcha, J., Niamsup, P. New exponential passivity of BAM neural networks with time-varying delays. Neural Comput & Applic 29, 1593–1600 (2018). https://doi.org/10.1007/s00521-016-2657-1

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  • DOI: https://doi.org/10.1007/s00521-016-2657-1

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