Robust synchronization analysis of delayed fractional order neural networks with uncertain parameters

Xinxin Zhang; Yunpeng Ma; Shan Gao; Jiancai Song; Lei Chen; Xinxin Zhang; Yunpeng Ma; Shan Gao; Jiancai Song; Lei Chen

doi:10.3934/math.20221040

AIMS Mathematics

2022, Volume 7, Issue 10: 18883-18896. doi: 10.3934/math.20221040

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Robust synchronization analysis of delayed fractional order neural networks with uncertain parameters

School of Information Engineering, Tianjin University of Commerce, Guangrongdao Road 409, Tianjin, China

Received: 15 June 2022 Revised: 27 July 2022 Accepted: 12 August 2022 Published: 25 August 2022
MSC : 93A30, 93D09

This paper is concerned with the robust synchronization analysis of delayed fractional order neural networks with uncertain parameters (DFNNUPs). Firstly, the DFNNUPs drive system model and response system model are established. Secondly, using multiple matrix quadratic Lyapunov function approach and inequality analysis technique, the robust synchronization conditions are derived in the form of the matrix inequalities. Finally, the correctness of the theoretical results is verified by an example.
- robust synchronization,
- delayed fractional order neural networks,
- uncertain parameters,
- multiple matrix quadratic Lyapunov function,
- matrix inequalities
Citation: Xinxin Zhang, Yunpeng Ma, Shan Gao, Jiancai Song, Lei Chen. Robust synchronization analysis of delayed fractional order neural networks with uncertain parameters[J]. AIMS Mathematics, 2022, 7(10): 18883-18896. doi: 10.3934/math.20221040

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Abstract

This paper is concerned with the robust synchronization analysis of delayed fractional order neural networks with uncertain parameters (DFNNUPs). Firstly, the DFNNUPs drive system model and response system model are established. Secondly, using multiple matrix quadratic Lyapunov function approach and inequality analysis technique, the robust synchronization conditions are derived in the form of the matrix inequalities. Finally, the correctness of the theoretical results is verified by an example.

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