Global synchronization of uncertain fractional-order BAM neural networks with time delay via improved fractional-order integral inequality

M. Shafiya, G. Nagamani and D. Dafik

Mathematics and Computers in Simulation (MATCOM), 2022, vol. 191, issue C, 168-186

Abstract: This paper is concerned with the problem of global synchronization for a class of uncertain fractional-order bidirectional associative memory (FOBAM) neural networks (NNs) with constant time delay. Some linear matrix inequality (LMI) based conditions have been established for ensuring the stability behavior of the governing augmented system derived from the considered fractional-order master system and the constructed slave system. In such LMI conditions, for utilizing the information on the time delay terms and the order of the fractional derivative, some fractional-order integral inequalities have been developed. Next, by defining a new Lyapunov–Krasovskii functional (LKF) and based on the improved fractional-order inequalities, the global synchronization criteria have been established in terms of a solvable set of LMIs for the considered class of uncertain FOBAM NNs without uncertain matrices. Further, the obtained results have been extended to the case of uncertain FOBAM NNs. Finally, a numerical example is presented to illustrate the feasibility and effectiveness of the proposed theoretical results.

Keywords: Caputo’s fractional derivative; Uncertainty; Fractional-order neural networks; Lyapunov–Krasovskii functional; Linear matrix inequalities (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:191:y:2022:i:c:p:168-186

DOI: 10.1016/j.matcom.2021.08.001

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