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Fixed-Time Anti-synchronization and Preassigned-Time Synchronization of Discontinuous Fuzzy Inertial Neural Networks with Bounded Distributed Time-Varying Delays

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Abstract

This paper is dedicated to fixed-time anti-synchronization (FXTAS) and preassigned-time synchronization (PATS) of discontinuous fuzzy inertial neural networks with mixed time-varying delays. Different from the traditional continuous neural network model, the differential inclusion theory is utilized to deal with discontinuous systems. Then, based on Lyapunov stability theory, two operational and efficient pure power-law control schemes are designed to ensure FXTAS and PATS. PATS is more flexible than fixed-time synchronization because its settling-time can be set in advance according to the actual situation. Finally, two numerical simulations are given to confirm the consistency with the theoretical results obtained in this paper.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China Nos. 61976228 and 61876192.

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Correspondence to Guodong Zhang.

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Liu, Y., Zhang, G. & Hu, J. Fixed-Time Anti-synchronization and Preassigned-Time Synchronization of Discontinuous Fuzzy Inertial Neural Networks with Bounded Distributed Time-Varying Delays. Neural Process Lett 55, 3333–3353 (2023). https://doi.org/10.1007/s11063-022-11011-4

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