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Finite-Time Synchronization for T–S Fuzzy Complex-Valued Inertial Delayed Neural Networks Via Decomposition Approach

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Abstract

This paper is mainly dedicated to the issue of finite-time synchronization of T–S fuzzy complex-valued neural networks with time-varying delays and inertial terms via directly constructing Lyapunov functions with separating the original complex-valued neural networks into two real-valued subsystems equivalently. First of all, to facilitate the analysis of the second-order derivative caused by the inertial term, two intermediate variables are introduced to transfer complex-valued inertial delayed neural networks (CVIDNNs) into the first-order differential equation form. Next, CVIDNNs are developed using T–S fuzzy rules. By using the Lyapunov stability theory, inequality scaling skills and adjustable algebraic criteria for T–S fuzzy CVIDNNs as well as the upper bound of the settling time for synchronization, are derived. Finally, one numerical example with simulations is given to illustrate the effectiveness of our theoretical results.

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Acknowledgements

This work was supported by the National Board Higher Mathematics, Mumbai, India, under the Sanctioned No. 02011/10/2019/NBHM(R.P)/R D II / 1242 and the National Natural Science Foundation of China under Grant No. 62103103, and the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20210223.

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Authors and Affiliations

  1. Department of Mathematics, Thiruvalluvar University, Vellore, 632 115, India

    S. Ramajayam & R. Samidurai

  2. Department of Mathematics, Adhiparasakthi College of Arts and Science (Autonomous), Ranipet, 632 506, India

    S. Rajavel

  3. School of Cyber Science and Engineering, Southeast University, Nanjing, 211189, China

    Yang Cao

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  1. S. Ramajayam
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  2. S. Rajavel
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  3. R. Samidurai
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  4. Yang Cao
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Correspondence to Yang Cao.

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Ramajayam, S., Rajavel, S., Samidurai, R. et al. Finite-Time Synchronization for T–S Fuzzy Complex-Valued Inertial Delayed Neural Networks Via Decomposition Approach. Neural Process Lett 55, 5885–5903 (2023). https://doi.org/10.1007/s11063-022-11117-9

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