New Stability Criteria for Neutral Cohen–Grossberg Neural Networks with Discrete Delays
WSEAS Transactions on Systems and Control, cilt.21, ss.130-139, 2026 (Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 21
- Basım Tarihi: 2026
- Doi Numarası: 10.37394/23203.2026.21.14
- Dergi Adı: WSEAS Transactions on Systems and Control
- Derginin Tarandığı İndeksler: Scopus, INSPEC
- Sayfa Sayıları: ss.130-139
- Anahtar Kelimeler: Cohen-Grossberg Neural Networks, Discrete Delay Terms, Lyapunov Functionals, Matrix Theory, Neutral Systems, Stability Analysis
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- İstanbul Üniversitesi-Cerrahpaşa Adresli: Evet
Özet
This research study examines global asymptotic stability of Cohen–Grossberg neural networks that involve both discrete time delay terms in neuron states and neutral delay terms in the time derivatives of neurons’ states. In this context, a suitable Lyapunov functional, constructed as a linear combination of three complementary main Lyapunov functional candidates, is employed to present new alternative sufficient criteria guaranteeing global asymptotic stability of neutral-type neural system possessing discrete delay components. The obtained criteria are established through explicit algebraic inequalities that utilize key matrix properties and structural characteristics of the system functions. The proposed results are basically stated in terms of parameters associated with the considered neutral neural system. These conditions are completely independent of the delay terms and can directly be tested by checking a set of simple algebraic inequalities. In order to illustrate some efficiency aspects of the derived stability results, a numerical example is analyzed.