Sine-Transform-Based Dual-Memristor Hyperchaotic Map and Analog Circuit Implementation

Memristor, as a fundamental circuit element with strong nonlinearities, particularly a periodic memristor, plays a key role in constructing discrete maps with significant chaotic complexity. However, the initial boosting of extreme multistability remains insufficiently explored in current studies. To further improve the dynamical behavior and complexity of discrete memristor maps, a dual-memristor self-feedback and sinusoidal transformation modeling strategy is proposed. Control parameters-related dynamical properties and specifically memristor initial-boosted dynamical performances are further investigated through one/two-dimensional (1/2-D) bifurcation diagrams, 1/2-D Lyapunov exponents, and attractor phase diagrams. Numerical results demonstrate that the suggested map not only generates rich dynamics but also exhibits excellent initial-boosted extreme multistability. Furthermore, the performance evaluations are carried out on strange attractors, confirming the enhanced complexity of the hyperchaotic attractor through noteworthy complexity indicators. Then, an analog circuit is developed and implemented to validate the accuracy of the numerical results further. Finally, a novel color multi-image encryption strategy based on enhanced hyperchaotic sequences is proposed, and its security and performance are thoroughly analyzed to prove robustness and reliability, increasing its practical application value.

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Publication Details

Journal
IEEE Transactions on Instrumentation and Measurement
Published
2025-01-01
DOI
https://doi.org/10.1109/tim.2025.3570347
Citations
24
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
17.53

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article

Sine-Transform-Based Dual-Memristor Hyperchaotic Map and Analog Circuit Implementation

Shaohua Zhang, Hongli Zhang, Cong Wang, Z. J. Chong
24 citations
IEEE Transactions on Instrumentation and Measurement
Advanced Memory and Neural Computing
17.53
article

Sine-Transform-Based Dual-Memristor Hyperchaotic Map and Analog Circuit Implementation

Shaohua Zhang, Hongli Zhang, Cong Wang, Z. J. Chong
article en
24 citations

Abstract

Memristor, as a fundamental circuit element with strong nonlinearities, particularly a periodic memristor, plays a key role in constructing discrete maps with significant chaotic complexity. However, the initial boosting of extreme multistability remains insufficiently explored in current studies. To further improve the dynamical behavior and complexity of discrete memristor maps, a dual-memristor self-feedback and sinusoidal transformation modeling strategy is proposed. Control parameters-related dynamical properties and specifically memristor initial-boosted dynamical performances are further investigated through one/two-dimensional (1/2-D) bifurcation diagrams, 1/2-D Lyapunov exponents, and attractor phase diagrams. Numerical results demonstrate that the suggested map not only generates rich dynamics but also exhibits excellent initial-boosted extreme multistability. Furthermore, the performance evaluations are carried out on strange attractors, confirming the enhanced complexity of the hyperchaotic attractor through noteworthy complexity indicators. Then, an analog circuit is developed and implemented to validate the accuracy of the numerical results further. Finally, a novel color multi-image encryption strategy based on enhanced hyperchaotic sequences is proposed, and its security and performance are thoroughly analyzed to prove robustness and reliability, increasing its practical application value.

IEEE Transactions on Instrumentation and MeasurementVol. 74
Xinjiang University (CN)
Natural Science Foundation of Xinjiang Province, National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 1%
Advanced Memory and Neural Computing
17.53
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Sine-Transform-Based Dual-Memristor Hyperchaotic Map and Analog Circuit Implementation — Shaohua Zhang, Hongli Zhang, et al. · IEEE Transactions on Instrumentation and Measurement (2025) | TGRS Research Map | TGRS