Semi-Global Polynomial Synchronization of High-Order Multiple Proportional-Delay BAM Neural Networks

Er-yong Cong (), Xian Zhang () and Li Zhu
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Er-yong Cong: Department of Mathematics, Harbin University, Harbin 150086, China
Xian Zhang: School of Mathematical Science, Heilongjiang University, Harbin 150080, China
Li Zhu: Department of Mathematics, Harbin University, Harbin 150086, China

Mathematics, 2025, vol. 13, issue 9, 1-16

Abstract: This paper addresses the semi-global polynomial synchronization (SGPS) problem for a class of high-order bidirectional associative memory neural networks (HOBAMNNs) with multiple proportional delays. The time-delay-dependent semi-global polynomial stability criterion for error systems was established via a direct approach. The derived stability conditions are formulated as several simple inequalities that are readily solvable, facilitating direct verification using standard computational tools (e.g., YALMIP). Notably, this method can be applied to many system models with proportional delays after minor modifications. Finally, a numerical example is provided to validate the effectiveness of the theoretical results.

Keywords: semi-global polynomial stability; high-order BAM neural network; semi-global polynomial synchronization; proportional delays; controller gains (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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