{
  "annotation_id": "9a3c673e-5838-4f66-8b66-6e8a8e93bf84",
  "date_created": "2026-02-17T05:53:08.708000Z",
  "date_modified": "2026-02-17T05:53:08.708000Z",
  "file_hash": "4e358bfe7191372e0dc8770ab3a3899741fa50ff8c6d16a35c840dd82c4db174",
  "private": false,
  "record": {
    "abstract": "Vibrational resonance focuses on the resonance behavior of a nonlinear system when it is subjected to both a weak low-frequency characteristic signal and a high-frequency auxiliary signal. A traditional Duffing system has a fixed natural frequency and lacks adaptability to the excitation frequency, resulting in vibrational resonance occurring only in a lower frequency range, which affects the application of vibrational resonance. We propose a frequency-adaptive learning Duffing system to overcome the above problem through a learning rule of the natural frequency. The optimal vibrational resonance performance is demonstrated by examining the influence of auxiliary signal parameters, nonlinear stiffness coefficient and the learning rule on the response. The appearance of vibrational resonance is verified by numerical simulation, approximated theoretical predication and circuit simulation. In addition, the advantages of the proposed frequency-adaptive learning rule are highlighted in vibrational resonance performance by comparing with that of two other commonly used alternatives called Hebbian learning rules. The proposed learning rule makes the system more stable and have a stronger resonance degree. The results provide a useful reference for optimizing nonlinear system response and also for processing a weak characteristic signal through nonlinear resonance methods. These achievements provide a groundbreaking foundation for future applied studies especially in the field of weak and complex signal processing.",
    "arxiv_id": "2601.06539",
    "authors": [
      "Zhongqiu Wan",
      "Jianhua Yang",
      "Feng Tian",
      "Huatao Chen",
      "Miguel A. F. Sanju\u00e1n"
    ],
    "categories": [
      "nlin.AO"
    ],
    "doi": "10.1016/j.chaos.2025.117821",
    "journal_ref": "Chaos, Solitons \u0026 Fractals 204, 117821 (2026)",
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Vibrational resonance in a frequency-adaptive learning Duffing system",
    "url": "https://arxiv.org/abs/2601.06539",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "a8034dba-1bb1-4d22-9037-13c344dc4a01",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}