Exponential and almost sure exponential stability of stochastic fuzzy delayed Cohen–Grossberg neural networks
✍ Scribed by Quanxin Zhu; Xiaodi Li
- Book ID
- 116496101
- Publisher
- Elsevier Science
- Year
- 2012
- Tongue
- English
- Weight
- 440 KB
- Volume
- 203
- Category
- Article
- ISSN
- 0165-0114
No coin nor oath required. For personal study only.
📜 SIMILAR VOLUMES
The exponential stability characteristics of the Cohen-Grossberg neural networks with discrete delays are studied in this paper, without assuming the symmetry of connection matrix as well as the monotonicity and differentiability of the activation functions and the self-signal functions. By construc
The stability of stochastic delayed Hopfield neural networks (DHNN) is investigated in this paper. Under the help of suitable Lyapunov function and the semimartingale convergence theorem, we obtain some sufficient criteria to check the almost sure exponential stability of the DHNN.
In this paper, we study the impulsive stochastic Cohen-Grossberg neural networks with mixed delays. By establishing an Loperator differential inequality with mixed delays and using the properties of M-cone and stochastic analysis technique, we obtain some sufficient conditions ensuring the exponenti