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New Results on Finite/Fixed-Time Stabilization of Stochastic Second-Order Neutral-Type Neural Networks with Mixed Delays

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Abstract

This paper states with the objective of investigating the finite-time stabilization and fixed-time stabilization analysis for stochastic second-order neutral-type neural networks with mixed delays. By using a variable transformation, we first rewrite the original system as a first-order differential system. By designing some feedback control laws inputs, stochastic analysis theory, finite-time stability theorem, fixed-time stability theorem, based on Lyapunov Functionals and inequalities techniques, new sufficient conditions ensuring the finite/fixed-time stabilization of the suggested system are given. Finally, the developed main control schemes, the finite/fixed-time stabilization for the stochastic Neural Networks are confirmed by two simulations examples.

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Funding

This work was jointly supported by the National Natural Science Foundation of China (62173139, 61773217), the Science and Technology Innovation Program of Hunan Province (2021RC4030), Hunan Provincial Science and Technology Project Foundation (2019RS1033).

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Authors and Affiliations

  1. Department of Mathematics, GAMA Laboratory LR21ES10, Faculty of Sciences of Bizerta, University of Carthage, BP W, 7021, Zarzouna, Bizerta, Tunisia

    Chaouki Aouiti & Hediene Jallouli

  2. CHP-LCOCS, School of Mathematics and Statistics, Hunan Normal University, Changsha, 410081, Hunan, People’s Republic of China

    Quanxin Zhu

  3. Science Program, Texas A & M University at Qatar, 23874, Doha, Qatar

    Tingwen Huang

  4. School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu, 610106, People’s Republic of China

    Kaibo Shi

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  1. Chaouki Aouiti
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  2. Hediene Jallouli
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  3. Quanxin Zhu
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  4. Tingwen Huang
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Correspondence to Quanxin Zhu.

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Aouiti, C., Jallouli, H., Zhu, Q. et al. New Results on Finite/Fixed-Time Stabilization of Stochastic Second-Order Neutral-Type Neural Networks with Mixed Delays. Neural Process Lett 54, 5415–5437 (2022). https://doi.org/10.1007/s11063-022-10868-9

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