Results for Chaos Synchronization with New Multi-Fractional Order of Neural Networks by Multi-Time Delay

Fatin Nabila Abd Latiff, Wan Ainun Mior Othman and Inés P. Mariño

Complexity, 2021, vol. 2021, 1-17

Abstract: A new finding is proposed for multi-fractional order of neural networks by multi-time delay (MFNNMD) to obtain stable chaotic synchronization. Moreover, our new result proved that chaos synchronization of two MFNNMDs could occur with fixed parameters and initial conditions with the proposed control scheme called sliding mode control (SMC) based on the time-delay chaotic systems. In comparison, the fractional-order Lyapunov direct method (FLDM) is proposed and is implemented to SMC to maintain the systems’ sturdiness and assure the global convergence of the error dynamics. An extensive literature survey has been conducted, and we found that many researchers focus only on fractional order of neural networks (FNNs) without delay in different systems. Furthermore, the proposed method has been tested with different multi-fractional orders and time-delay values to find the most stable MFNNMD. Finally, numerical simulations are presented by taking two MFNNMDs as an example to confirm the effectiveness of our control scheme.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:9398333

DOI: 10.1155/2021/9398333

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