Memristive multi-wing chaotic Hopfield neural network for LiDAR data security
By applying the synapse-like electrical element, memristor, complex chaotic dynamics can be generated in Hopfield neural networks. However, the multi-wing butterfly chaotic attractor generated by the memristive Hopfield neural network remains undiscovered. In this paper, we introduce a novel chaotic multi-wing butterfly generation method within the Hopfield neural network (HNN). Our proposed approach incorporates a piecewise linear memristor to establish coupling between two neurons in a three-neuronal HNN. This design allows straightforward control over the number of butterfly wings by adjusting the memristor parameters. We conduct a comprehensive numerical analysis of the chaotic butterfly dynamics using phase portraits, Lyapunov exponent spectra, state variable bifurcation diagrams, and bi-parameter dynamical maps. Furthermore, the proposed model is implemented based on the digital circuit FPGA platform and its correctness is verified through experiments. Moreover, we leverage the developed chaotic multi-wing butterfly to construct a secure LiDAR point cloud system. The system employs a chaotic permutation and diffusion algorithm based on the proposed multi-wing butterfly. Security performance and time efficiency are evaluated using multiple numerical methods, and the results demonstrate the effectiveness of the proposed LiDAR data secure system.
Item Type | Article |
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Additional information | © 2025, The Author(s), under exclusive licence to Springer Nature B.V. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1007/s11071-025-10982-y |
Keywords | data secure, fpga implementation, memristive neural network, multi-wing attractor, control and systems engineering, aerospace engineering, ocean engineering, mechanical engineering, electrical and electronic engineering, applied mathematics |
Date Deposited | 15 May 2025 15:50 |
Last Modified | 31 May 2025 00:47 |
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picture_as_pdf - ND_LiDAR.pdf
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subject - Submitted Version
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lock_clock - Restricted to Repository staff only until 20 February 2026
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copyright - Available under Unspecified