Global polynomial synchronization for quaternion-valued T–S fuzzy inertial neural networks via event-triggered control: A polynomial gain method

Jingjing Zhang, Zhouhong Li, Jinde Cao, Mahmoud Abdel-Aty and Xiaofang Meng

Chaos, Solitons & Fractals, 2025, vol. 196, issue C

Abstract: This work explores the global polynomial synchronization for a class of quaternion-valued Takagi–Sugeno fuzzy inertial neural networks based on event-triggered control. Firstly, the paper designs the fuzzy event-triggered controller with a polynomial gain, a unique approach to optimize the event-triggered mechanism. The non-reduced order and non-decomposition methods are applied to maintain computational efficiency without introducing new variables. Then, under static and dynamic event-triggered conditions, the system’s global polynomial synchronization is guaranteed by formulating a suitable delay-free Lyapunov functional and using quaternion properties and inequality techniques. Moreover, rigorous derivation is employed to verify a positive lower bound of any event-triggered interval, concluding that the system does not produce Zeno behavior. Finally, a numerical example and the application of image encryption and decryption are presented to strongly validate the reliability of the model and control mechanism in achieving global polynomial synchronization.

Keywords: Quaternion-valued inertial neural networks; Event-triggered control; Polynomial control gain; Takagi–Sugeno fuzzy models; Global polynomial synchronization (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:196:y:2025:i:c:s0960077925004163

DOI: 10.1016/j.chaos.2025.116403

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