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Offset boosting-dominated on-demand multistability editing in memristive neural network

Author

Listed:
  • Li, Yongxin
  • Zhang, Sen
  • Wang, Yichen
  • Lei, Tengfei
  • Liu, Qinghao
  • Yao, Yanchang
  • Chen, Bo

Abstract

A central challenge in memristive neural networks is to generate complex attractors while organizing their coexistence in a controllable geometric manner. This paper develops a fully connected memristive Hopfield neural network by embedding two complementary memristive mechanisms, one acting as the neuron-level autapse and the other representing the external electromagnetic radiation. With an attractor-doubling perspective, a non-bifurcation offset-boosting parameter enables unified synthesis of multiscroll chaos and grid-plane multiscroll multistability. The spacing among coexisting attractors becomes tunable rather than fixed, and coexistence patterns beyond uniform tiling are obtained. Memristor-related parameters further provide amplitude rescaling in the associated dimensions, supporting joint control of initial boosting and oscillation amplitude. Experimental validation is carried out via an MCU-based digital realization, and a multi-strength multistable pseudo-random number generator is constructed and evaluated.

Suggested Citation

  • Li, Yongxin & Zhang, Sen & Wang, Yichen & Lei, Tengfei & Liu, Qinghao & Yao, Yanchang & Chen, Bo, 2026. "Offset boosting-dominated on-demand multistability editing in memristive neural network," Chaos, Solitons & Fractals, Elsevier, vol. 208(P3).
    • Handle: RePEc:eee:chsofr:v:208:y:2026:i:p3:s096007792600408x
    DOI: 10.1016/j.chaos.2026.118267
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