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      Various Attractors, Coexisting Attractors and Antimonotonicity in a Simple Fourth-Order Memristive Twin-T Oscillator

      1 , 2 , 1 , 1 , 1
      International Journal of Bifurcation and Chaos
      World Scientific Pub Co Pte Lt

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          Abstract

          By replacing the resistor in a Twin-T network with a generalized flux-controlled memristor, this paper proposes a simple fourth-order memristive Twin-T oscillator. Rich dynamical behaviors can be observed in the dynamical system. The most striking feature is that this system has various periodic orbits and various chaotic attractors generated by adjusting parameter [Formula: see text]. At the same time, coexisting attractors and antimonotonicity are also detected (especially, two full Feigenbaum remerging trees in series are observed in such autonomous chaotic systems). Their dynamical features are analyzed by phase portraits, Lyapunov exponents, bifurcation diagrams and basin of attraction. Moreover, hardware experiments on a breadboard are carried out. Experimental measurements are in accordance with the simulation results. Finally, a multi-channel random bit generator is designed for encryption applications. Numerical results illustrate the usefulness of the random bit generator.

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          Most cited references26

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          A hyper-chaos-based image encryption algorithm using pixel-level permutation and bit-level permutation

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            Dynamics of self-excited attractors and hidden attractors in generalized memristor-based Chua’s circuit

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              Memristor-based cellular nonlinear/neural network: design, analysis, and applications.

              Cellular nonlinear/neural network (CNN) has been recognized as a powerful massively parallel architecture capable of solving complex engineering problems by performing trillions of analog operations per second. The memristor was theoretically predicted in the late seventies, but it garnered nascent research interest due to the recent much-acclaimed discovery of nanocrossbar memories by engineers at the Hewlett-Packard Laboratory. The memristor is expected to be co-integrated with nanoscale CMOS technology to revolutionize conventional von Neumann as well as neuromorphic computing. In this paper, a compact CNN model based on memristors is presented along with its performance analysis and applications. In the new CNN design, the memristor bridge circuit acts as the synaptic circuit element and substitutes the complex multiplication circuit used in traditional CNN architectures. In addition, the negative differential resistance and nonlinear current-voltage characteristics of the memristor have been leveraged to replace the linear resistor in conventional CNNs. The proposed CNN design has several merits, for example, high density, nonvolatility, and programmability of synaptic weights. The proposed memristor-based CNN design operations for implementing several image processing functions are illustrated through simulation and contrasted with conventional CNNs. Monte-Carlo simulation has been used to demonstrate the behavior of the proposed CNN due to the variations in memristor synaptic weights.
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                Author and article information

                Journal
                International Journal of Bifurcation and Chaos
                Int. J. Bifurcation Chaos
                World Scientific Pub Co Pte Lt
                0218-1274
                1793-6551
                May 04 2018
                April 2018
                May 04 2018
                April 2018
                : 28
                : 04
                : 1850050
                Affiliations
                [1 ]College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, P. R. China
                [2 ]Department of Electronic and Information Engineering, Hunan University of Science and Engineering, Yongzhou 425199, P. R. China
                Article
                10.1142/S0218127418500505
                ef2b4176-75d7-45cd-93b1-715b6c2e62c7
                © 2018
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