Memristive hyperchaotic multi-attractor two-neuron Hopfield neural network: Dynamics analysis and application to hardware image encryption

Yuheng Chen, Sen Zhang, Xin Ding, Xudong Gao, Yichen Wang and Zilu Wang

Chaos, Solitons & Fractals, 2026, vol. 209, issue P1

Abstract: Chaotic systems are ideal for cryptographic applications because of their intrinsic unpredictability and extreme dependence to parameters and initial states. In this study, a discrete memristor model featuring a multi-segment internal state equation is integrated within a two-neuron Hopfield neural network. This integration yields a novel memristive hyperchaotic multi-attractor two-neuron Hopfield neural network (MHMT-HNN). The Lyapunov exponents analysis shows that the MHMT-HNN possesses strong hyperchaotic robustness beyond a few exceptionally narrow chaotic intervals. In addition, the MHMT-HNN exhibits initially offset-switched homogeneous coexisting zigzag hyperchaotic attractors, indicating homogeneous multistability arises in this network. With appropriate parameter settings, the MHMT-HNN can generate complex multi-zigzag and multi-meander hyperchaos. These dynamical features are implemented and verified on an STM32 microcontroller platform. Furthermore, a lightweight image encryption algorithm utilizing the MHMT-HNN is developed to meet the secure transmission requirements of image data in the Internet of Things (IoT). The algorithm exhibits excellent encryption performance and low computational cost, as demonstrated by test results.

Keywords: Chaos; Hopfield neural network; Hardware encryption; Homogeneous multistability; Internet of Things (IoT) (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:209:y:2026:i:p1:s0960077926006338

DOI: 10.1016/j.chaos.2026.118492

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