Work

Projective Synchronization of Quaternion-Valued Discontinuous Competitive Neural Networks with Multiple Time Scales

Year: 2025

Type: article

Abstract: In this study, we explore the projective synchronization of quaternion-valued competitive neural networks with multiple time scales (QVMTSCNNs), while analyzing the impacts of discontinuous activation functions and time delays. To achieve the control goal, two novel quaternion controllers are designed, which do not depend on the ratio of the fast and slow time scales. By applying the nonsmooth analysis and quaternion inequality techniques, two novel theorems for projective synchronization of QVMTSCNNs are derived by non-separating methods. The obtained results in this study are relatively simpler and straightforward, extending some previous findings. Lastly, numerical analyses are executed to substantiate the theoretical conclusions.

Source: Applied Mathematics and Statistics

DOI: 10.53941/ams.2025.100002

Authors: Yu Zheng, Ruoyu Wei, Zixuan Chen

Institution: Nanjing University of Information Science and Technology

Language: English

Topic: Neural Networks Stability and Synchronization

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Natural Science Foundation of China

Award: G4668539408