Novel compound multistable stochastic resonance weak signal detection.
The research on stochastic resonance (SR) which is used to extract weak signals from noisy backgrounds is of great theoretical significance and promising application. To address the shortcomings of the classical tristable SR model, this article proposes a novel compound multistable stochastic resona...
| Publicado en: | Zeitschrift für Naturforschung Section A: A Journal of Physical Sciences Vol. 79; no. 4; pp. 329 - 345 |
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| Autores principales: | , , , , , , |
| Formato: | Artículo |
| Publicado: |
De Gruyter
Apr2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=176410353&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 176410353 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 09320784 FL07 jtl: Zeitschrift für Naturforschung Section A: A Journal of Physical Sciences issn: 09320784 maglogo: N pubinfo: dt: Apr2024 vid: 79 iid: 4 pid: 1734 pub: De Gruyter artinfo: ui: 176410353 10.1515/zna-2023-0312 ppf: 329 ppct: 16 formats: tig: atl: Novel compound multistable stochastic resonance weak signal detection. aug: au: Jiao, Shangbin Xue, Qiongjie Li, Na Gao, Rui Lv, Gang Wang, Yi Li, Yvjun affil: Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an, 710048, China College of Humanities and Management, Xi'an Traffic Engineering Institute, Xi'an, 710065, China School of Electronic and Electrical Engineering, Baoji University of Arts and Sciences, Baoji, 721016, China Huaneng Weihai Power Generation Co. Ltd, Weihai, 264200, China su: Stochastic resonance Signal detection Optimization algorithms Image processing Performance theory sug: subj: Stochastic resonance Signal detection Optimization algorithms Image processing Performance theory keyword: compound multistable model image processing stochastic resonance weak signal dectection Woods–Saxon ab: The research on stochastic resonance (SR) which is used to extract weak signals from noisy backgrounds is of great theoretical significance and promising application. To address the shortcomings of the classical tristable SR model, this article proposes a novel compound multistable stochastic resonance (NCMSR) model by combining the Woods–Saxon (WS) and tristable models. The influence of the parameters of the NCMSR systems on the output response performance is studied under different α stable noises. Meanwhile, the adaptive synchronization optimization algorithm based on the proposed model is employed to achieve periodic and non-periodic signal identifications in α stable noise environments. The results show that the proposed system model outperforms the tristable system in terms of detection performance. Finally, the NCMSR model is applied to 2D image processing, which achieves great noise reduction and image recovery effects. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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