Yang F, Wang W, Li L, Zheng M, Zhang Y, Liang Z. Finite-time parameter identification of fractional-order time-varying delay neural networks based on synchronization.
CHAOS (WOODBURY, N.Y.) 2023;
33:033146. [PMID:
37003798 DOI:
10.1063/5.0137598]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 03/03/2023] [Indexed: 06/19/2023]
Abstract
We research the finite-time parameter identification of fractional-order time-varying delay neural networks (FTVDNNs) based on synchronization. First, based on the fractional-order Lyapunov stability theorem and feedback control idea, we construct a synchronous controller and some parameter update rules, which accomplish the synchronization of the drive-response FTVDNNs and complete the identification of uncertain parameters. Second, the theoretical analysis of the synchronization method is carried out, and the stable time is calculated. Finally, we give two examples for simulation verification. Our method can complete the synchronization of the FTVDNNs in finite time and identify uncertain parameters while synchronizing.
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