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Quasi-synchronization of stochastic memristor-based neural networks with mixed delays and parameter mismatches

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Abstract

This paper is concerned with quasi-synchronization of stochastic memristor-based neural networks with mixed delays and parameter mismatches. Due to the parameter mismatches, mean-square exponential synchronization generally cannot be achieved directly, then the concept of exponential quasi-synchronization in mean square is introduced. Furthermore, based on the differential inclusions theory, stochastic Lyapunov function method and inequality techniques, some sufficient conditions are derived to guarantee the mean-square exponential quasi-synchronization for stochastic memristor-based neural networks with mixed delays. Finally, two examples are given to show the effectiveness of the proposed theoretical results.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 61503046, 11547006 and 61773401).

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Correspondence to Yinfang Song.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.

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Song, Y., Zeng, Z., Sun, W. et al. Quasi-synchronization of stochastic memristor-based neural networks with mixed delays and parameter mismatches. Neural Comput & Applic 32, 4615–4628 (2020). https://doi.org/10.1007/s00521-018-3772-y

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