Work

Global Polynomial Synchronization of Quaternion‐Valued Inertial Neural Networks With Proportional Delay and Mismatched Parameters

Year: 2025

Type: article

Abstract: ABSTRACT This paper investigates the global polynomial synchronization of the quaternion‐valued inertial neural networks with proportional delay and mismatched parameters. The global polynomial synchronization of neural networks is guaranteed by constructing the suitable Lyapunov functional and controller, utilizing nonreduction and nondecomposition methods. Notably, the Lyapunov functional established is delay‐free, and choosing the appropriate norm of the quaternion vectors can more effectively reduce the Lyapunov functional. Moreover, some quaternion properties are applied to explore the global polynomial synchronization problem of quaternion‐valued inertial neural networks, which avoids quaternion decomposition. Finally, we validate the conclusions with the numerical simulation.

Source: Mathematical Methods in the Applied Sciences

DOI: 10.1002/mma.10948

Authors: Jingjing Zhang, Zhouhong Li, Jinde Cao, Xiaofang Meng

Institutions: Yunnan University of Finance And Economics, Yuxi Normal University, Southeast University

Language: English

Topic: Neural Networks Stability and Synchronization

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: Yunnan University of Finance and Economics, Yunnan University, Yunnan Provincial Department of Education

OpenAlex ID: W4409277205

FWCI: 1.342

Cites: 38

Cited by: 3

Related to: 10