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        A locally active discrete memristor model and its application in a hyperchaotic map

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        Author
        Ma, Minglin
        Yang, Yang
        Qiu, Zhicheng
        Peng, Yuexi
        Sun, Yichuang
        Li, Zhijun
        Wang, Mengjiao
        Attention
        2299/25372
        Abstract
        The continuous memristor is a popular topic of research in recent years, however, there is rare discussion about the discrete memristor model, especially the locally active discrete memristor model. This paper proposes a locally active discrete memristor model for the first time and proves the three fingerprints characteristics of this model according to the definition of generalized memristor. A novel hyperchaotic map is constructed by coupling the discrete memristor with a two-dimensional generalized square map. The dynamical behaviors are analyzed with attractor phase diagram, bifurcation diagram, Lyapunov exponent spectrum, and dynamic behavior distribution diagram. Numerical simulation analysis shows that there is significant improvement in the hyperchaotic area, the quasi-periodic area and the chaotic complexity of the two-dimensional map when applying the locally active discrete memristor. In addition, antimonotonicity and transient chaos behaviors of system are reported. In particular, the coexisting attractors can be observed in this discrete memristive system, resulting from the different initial values of the memristor. Results of theoretical analysis are well verified with hardware experimental measurements. This paper lays a great foundation for future analysis and engineering application of the discrete memristor and relevant the study of other hyperchaotic maps.
        Publication date
        2022-01-23
        Published in
        Nonlinear Dynamics
        Published version
        https://doi.org/10.1007/s11071-021-07132-5
        License
        Unspecified
        Other links
        http://hdl.handle.net/2299/25372
        Relations
        School of Physics, Engineering & Computer Science
        Metadata
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