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• W4313455568 abstract "<abstract><p>The dynamic signal transmission process can be regarded as an anti-periodic process, and fractional-order inertial neural networks are widely used in signal processing and other fields, so anti-periodicity is also regarded as an important dynamic feature of inertial neural networks. This paper mainly studies the existence and Mittag-Leffler stability of anti-periodic solutions for a class of fractional-order inertial BAM neural networks with time-delays. By introducing variable substitution, the model with two different fractional-order derivatives is transformed into a model with only one fractional-order derivative of the same order. Using the properties of fractional-order calculus, the relationship between the fractional-order integral of the state function with and without time-delays is given. Firstly, the sufficient conditions for the boundedness and the Mittag-Leffler stability of the solutions for the system are derived. Secondly, by constructing the sequence solution of the function for the system and applying Ascoli-Arzela theorem, the sufficient conditions for the existence and Mittag-Leffler stability of the anti-periodic solution are given. Finally, the correctness of the conclusion is verified by a numerical example.</p></abstract>" @default.
• W4313455568 created "2023-01-06" @default.
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• W4313455568 date "2023-01-01" @default.
• W4313455568 modified "2023-09-27" @default.
• W4313455568 title "The stability of anti-periodic solutions for fractional-order inertial BAM neural networks with time-delays" @default.
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• W4313455568 doi "https://doi.org/10.3934/math.2023312" @default.
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