Dynamical behavior and applications of fractional-order bicyclic crossed memristive neural networks with Neimark-Sacker bifurcation: Synchronization and image encryption.
basic_science · Level V
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- Record sourced from PubMed, PMID 42447816.
- Also identified by DOI 10.1016/j.neunet.2026.109326.
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Abstract
This study proposes a fractional-order NbO<sub>x</sub> memristor model and, based on this, introduces a novel fractional-order bicyclic crossed memristive neural network. The dynamic behaviors and firing patterns of this neural network are analyzed, with an emphasis on the Neimark-Sacker bifurcation induced by the fractional-order parameter: it is demonstrated that by keeping the system parameters constant and varying only the fractional order, a Neimark-Sacker bifurcation can be induced. The chaotic characteristics and bifurcations of this network are leveraged in a newly proposed image encryption algorithm that successfully passes various security analyses. Additionally, a synchronization controller is designed to regulate the generation of specific chaotic sequences by other neural networks, with the objective of approximating the chaotic sequences produced by the proposed neural network.