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A Novel High-Efficiency Variable Parameter Double Integration ZNN Model for Time-Varying Sylvester Equations

Author

Listed:
  • Zhe Peng

    (College of Computer Science and Engineering, Jishou University, Jishou 416000, China)

  • Yun Huang

    (College of Computer Science and Engineering, Jishou University, Jishou 416000, China)

  • Hongzhi Xu

    (College of Computer Science and Engineering, Jishou University, Jishou 416000, China)

Abstract

In this paper, a High-Efficiency Variable Parameter Double Integration Zeroing Neural Network (HEVPDIZNN) model combining variable parameter function and double integration is proposed to solve the time-varying Sylvester matrix equations, using the decreasing function with a large initial value as the variable parameter. This design achieves faster convergence and higher accuracy after stabilization.The use of double integral terms ensures that the model has higher solution accuracy and effectively suppresses constant noise, linear noise, and quadratic noise. The article proves the convergence and robustness of the model through theoretical analysis. In the comparison experiments with the existing models (MNTZNN, NTPVZNN, NSVPZNN, NSRNN, and ADIZNN), it is confirmed that HEVPDIZNN has faster convergence speed, the average error at the time of stabilization is about 10 − 5 times that of the existing models, and it has a better suppression of the linear noise, quadratic noise, and constant noise.

Suggested Citation

  • Zhe Peng & Yun Huang & Hongzhi Xu, 2025. "A Novel High-Efficiency Variable Parameter Double Integration ZNN Model for Time-Varying Sylvester Equations," Mathematics, MDPI, vol. 13(5), pages 1-26, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:5:p:706-:d:1596998
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    References listed on IDEAS

    as
    1. Xiao, Lin & Yi, Qian & Zuo, Qiuyue & He, Yongjun, 2020. "Improved finite-time zeroing neural networks for time-varying complex Sylvester equation solving," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 178(C), pages 246-258.
    2. Jin, Jie & Chen, Weijie & Qiu, Lixin & Zhu, Jingcan & Liu, Haiyan, 2023. "A noise tolerant parameter-variable zeroing neural network and its applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 482-498.
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