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Modified Newton Integration Neural Algorithm for Solving Time-Varying Yang-Baxter-Like Matrix Equation

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Abstract

This paper intends to solve the time-varying Yang-Baxter-like matrix equation (TVYBLME), which is frequently employed in the fields of scientific computing and engineering applications. Due to its critical and promising role, several methods have been constructed to generate a high-performing solution for the TVYBLME. However, given the fact that noise is ubiquitous and inevitable in actual systems. It is necessary to design a computational algorithm with strong robustness to solve the TVYBLME, which has rarely been mentioned previously. For this reason, to remedy shortcomings that the conventional computing methods have encountered in a noisy case, a modified Newton integration (MNI) neural algorithm is proposed and employed to solve the TVYBLME. In addition, the related theoretical analyses show that the proposed MNI neural algorithm has the noise-tolerance ability under various noisy cases. Finally, the feasibility and superiority of the proposed MNI neural algorithm to solve the TVYBLME are verified by simulation experiments.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Guangdong Ocean University (GDOU), Zhanjiang, 524025, China

    Haoen Huang, Zifan Huang, Chaomin Wu, Chengze Jiang, Dongyang Fu & Cong Lin

Authors
  1. Haoen Huang
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  2. Zifan Huang
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  3. Chaomin Wu
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  4. Chengze Jiang
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  5. Dongyang Fu
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  6. Cong Lin
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Correspondence to Dongyang Fu or Cong Lin.

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This work was supported by Key Projects of the Guangdong Education Department (2019KZDXM019); Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang) (ZJW-2019-08); High-Level Marine Discipline Team Project of Guangdong Ocean University (00202600-2009).

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Huang, H., Huang, Z., Wu, C. et al. Modified Newton Integration Neural Algorithm for Solving Time-Varying Yang-Baxter-Like Matrix Equation. Neural Process Lett 55, 773–787 (2023). https://doi.org/10.1007/s11063-022-10908-4

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  • DOI: https://doi.org/10.1007/s11063-022-10908-4

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