A General Framework for Stability Analysis of Neutral Cohen–Grossberg Neural Networks with Discrete Delay Terms
Mathematics, cilt.14, sa.17, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 14 Sayı: 17
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/math14173075
- Dergi Adı: Mathematics
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, zbMATH, Directory of Open Access Journals, Academic Search Ultimate (EBSCO), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: Cohen–Grossberg neural networks, Lyapunov functionals, matrix theory, neutral systems, stability analysis
- İstanbul Üniversitesi Adresli: Evet
Özet
This paper studies global asymptotic stability of Cohen–Grossberg neural networks involvingdiscrete time delays in the neuron states and neutral delays in the time derivatives ofthe neuron states. An appropriate Lyapunov functional, which is defined by the linearcombination of three Lyapunov functionals of the quadratic forms, is constructed to determinenew criteria for global asymptotic stability of neutral-type neural networks withdiscrete delay parameters. The proposed stability conditions are established through aset of algebraic inequalities that utilize key matrix properties and parameters of systemfunctions. These criteria are proved to be independent of delay components, and theycan be tested by checking some algebraic inequalities. A numerical example is studied toillustrate the efficiency aspects of the derived stability conditions.