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

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

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...

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Bibliographic Details
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
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Institution: Nanyang Technological University
Language: English
Description
Summary: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.

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