Synchronization of fractional-order memristor-based complex-valued neural networks with uncertain parameters and time delays

Abstract This paper talks about the global asymptotical synchronization problem of delayed fractional-order memristor-based complex-valued neural networks with uncertain parameters. Under the framework of Filippov solution and differential inclusion theory, several sufficient criteria ensuring the global asymptotical synchronization for the addressed drive-response models are derived, by means of Lyapunov direct method and comparison theorem. In addition, two numerical examples are designed to verify the correctness and effectiveness of the theoretical results.

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