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Stability and synchronization of fractional-order generalized reaction–diffusion neural networks with multiple time delays and parameter mismatch

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Abstract

In this paper, stability and synchronization of fractional-order generalized reaction–diffusion neural networks with multiple time delays and parameter mismatch are investigated. The global uniform stability conditions of fractional-order generalized reaction–diffusion neural networks are derived. Furthermore, considering parameter mismatch, the global synchronization conditions of fractional-order generalized reaction–diffusion neural networks with multiple time delays are given via the Lyapunov direct method. Finally, two numerical examples are presented to show the effectiveness of our theoretical results.

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Acknowledgements

This work is greatly funded by the National Natural Science Foundation of China under Grant Nos. 61903386 and 62173027, Beijing Municipal Natural Science Foundation under Grant No. Z180005, the Disciplinary Fundation of Central University of Finance and Economics, the Research Fund of Beijing Information Science & Technology University under Grant No. 2021XJJ64, and Qin Xin Talents Cultivation Program, Beijing Information Science & Technology University under Grant No. QXTCP C202119.

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Correspondence to Hu Wang.

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Gu, Y., Wang, H. & Yu, Y. Stability and synchronization of fractional-order generalized reaction–diffusion neural networks with multiple time delays and parameter mismatch. Neural Comput & Applic 34, 17905–17920 (2022). https://doi.org/10.1007/s00521-022-07414-y

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