Semi-Global Polynomial Synchronization of High-Order Multiple Proportional-Delay BAM Neural Networks
Abstract
:1. Introduction
- (1)
- This is the first study on the SGPS of multiple proportional-delay HOBAMNNs, and the definition of SGPS here is obviously different from that of GPS.
- (2)
- This paper proposes a direct derivation method based on the system’s solution. Different from previous research results, it avoids using nonlinear transformation to convert PDNNs into constant delay NNs. This method simplifies the research process, avoids the complexity brought by constructing the Lyapunov–Krasovskii functional, and makes the structure of the paper more reasonable.
- (3)
- The established SGPS criterion not only enhances the convergence rate and accuracy but also enables straightforward implementation using the MATLAB R2016b 9.1 toolbox.
2. Problem Description
3. Main Results
- (i)
- When , there is T and such that (11) is true, and
- (ii)
- When , there is T and such that (11) is true, and
4. Illustrative Examples
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Cong, E.-y.; Zhang, X.; Zhu, L. Semi-Global Polynomial Synchronization of High-Order Multiple Proportional-Delay BAM Neural Networks. Mathematics 2025, 13, 1512. https://doi.org/10.3390/math13091512
Cong E-y, Zhang X, Zhu L. Semi-Global Polynomial Synchronization of High-Order Multiple Proportional-Delay BAM Neural Networks. Mathematics. 2025; 13(9):1512. https://doi.org/10.3390/math13091512
Chicago/Turabian StyleCong, Er-yong, Xian Zhang, and Li Zhu. 2025. "Semi-Global Polynomial Synchronization of High-Order Multiple Proportional-Delay BAM Neural Networks" Mathematics 13, no. 9: 1512. https://doi.org/10.3390/math13091512
APA StyleCong, E.-y., Zhang, X., & Zhu, L. (2025). Semi-Global Polynomial Synchronization of High-Order Multiple Proportional-Delay BAM Neural Networks. Mathematics, 13(9), 1512. https://doi.org/10.3390/math13091512