In this paper, by means of constructing the extended impulsive delayed Halanay inequality and by Lyapunov functional methods, we analyze the global exponential stability and global attractivity of impulsive Hopfield neural networks with time delays. Some new sufficient conditions ensuring exponentia
β¦ LIBER β¦
Global attractivity and forward neural networks
β Scribed by M. Martelli; B. Johnston
- Publisher
- Elsevier Science
- Year
- 1996
- Tongue
- English
- Weight
- 412 KB
- Volume
- 9
- Category
- Article
- ISSN
- 0893-9659
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
Global exponential stability and global
β
Xilin Fu; Xiaodi Li
π
Article
π
2009
π
Elsevier Science
π
English
β 1014 KB
Global attraction and stability for Cohe
β
Kening Lu; Daoyi Xu; Zhichun Yang
π
Article
π
2006
π
Elsevier Science
π
English
β 812 KB
Approximation of state-space trajectorie
Approximation of state-space trajectories by locally recurrent globally feed-forward neural networks
β
Krzysztof Patan
π
Article
π
2008
π
Elsevier Science
π
English
β 466 KB
Global analysis of planar neural network
β
Fernanda Botelho; Valery A. Gaiko
π
Article
π
2006
π
Elsevier Science
π
English
β 125 KB
In this paper, the global qualitative analysis of cubic dynamical systems is established. These systems are used as learning models of planar neural networks.
Some global properties of neural network
β
Accardi, L. ;Aiello, A.
π
Article
π
1972
π
Springer-Verlag
β 569 KB
Invariance priors for Bayesian feed-forw
β
Udo v. Toussaint; Silvio Gori; Volker Dose
π
Article
π
2006
π
Elsevier Science
π
English
β 1001 KB
Neural networks (NN) are famous for their advantageous flexibility for problems when there is insufficient knowledge to set up a proper model. On the other hand, this flexibility can cause overfitting and can hamper the generalization of neural networks. Many approaches to regularizing NN have been