Analysis for Global Robust Stability of Cohen-Grossberg Neural Networks with Multiple Delays

Ji, Ce; Zhang, Huaguang; Guan, Huanxin

doi:10.1007/978-3-540-28647-9_17

Cited by 3 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus the equilibrium point is globally robust stable independent of the amplification function and time delays. However, the condition (16) in Theorem 2 is not satisfied since μ 2 = −0.5908 < 0. In a particular case, we let Thus, we have γ 1 = 3, γ 2 = 7, G 1 = 0.1, G 2 = 1.…”

Section: Examplesmentioning

confidence: 86%

“…REMARK 2. We notice that Theorem 2 in [2] obtained a similar condition to (16). In [2], the authors investigated the robust stability for interval Hopfield neural networks with time-varying delays.…”

Section: Global Robust Stabilitymentioning

confidence: 94%

“…(16) where a * ij = max(|a ij |, |a ij |), b * ij = max(|b ij |, |b ij |), then for each I ∈ R n , system (1) has a unique equilibrium point and it is globally robust stable.…”

Section: Global Robust Stabilitymentioning

confidence: 98%

“…In [15], the authors proved the exponential stability for this neural system directly and the robust stability followed by use of M-matrix. A LMI-based robust criterion was given in [16]. However, the condition involved the bounds of amplification function.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

New Results on the Robust Stability of Cohen–Grossberg Neural Networks with Delays

Rong

Chen

2006

Neural Process Lett

View full text Add to dashboard Cite

In this letter, a generalized type of Cohen-Grossberg neural networks with time delays are discussed and their global robust stability of the equilibrium point is investigated. By introducing a set of Lyapunov functionals, several new sufficient conditions guaranteeing the global robust convergence are derived. The results show that the amplification function a i (x) is harmless to the robust stability of Cohen-Grossberg neural networks. Two examples are given to demonstrate the applicability of the proposed results.

show abstract

Section: Examplesmentioning

confidence: 86%

Section: Global Robust Stabilitymentioning

confidence: 94%

“…(16) where a * ij = max(|a ij |, |a ij |), b * ij = max(|b ij |, |b ij |), then for each I ∈ R n , system (1) has a unique equilibrium point and it is globally robust stable.…”

Section: Global Robust Stabilitymentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations