Stability analysis of delayed neural networks via compound-parameter-based integral inequality

• Main Authors: Xue, Wenlong, Jin, Zhenghong, Tian, Yufeng
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Mathematical Sciences; Neural networks; Time-varying delay
• Online Access: https://hdl.handle.net/10356/179899
• Institution: Nanyang Technological University

This paper revisits the issue of stability analysis of neural networks subjected to time-varying delays. A novel approach, termed a compound-matrix-based integral inequality (CPBII), which accounts for delay derivatives using two adjustable parameters, is introduced. By appropriately adjusting these parameters, the CPBII efficiently incorporates coupling information along with delay derivatives within integral inequalities. By using CPBII, a novel stability criterion is established for neural networks with time-varying delays. The effectiveness of this approach is demonstrated through a numerical illustration.