Global exponential stability of interval neural networks with variable delays
نویسندگان
چکیده
منابع مشابه
Global exponential stability of cellular neural networks with variable delays
For cellular neural networks with time-varying delays, the problems of determining the exponential stability and estimating the exponential convergence rate are investigated by employing the Lyapunov–Krasovskii functional and linear matrix inequality (LMI) technique. A novel criterion for the stability, which give information on the delay-dependent property, is derived. Two examples are given t...
متن کاملGlobal Asymptotic and Exponential Stability of Tri-Cell Networks with Different Time Delays
In this paper, a bidirectional ring network with three cells and different time delays is presented. To propose this model which is a good extension of three-unit neural networks, coupled cell network theory and neural network theory are applied. In this model, every cell has self-connections without delay but different time delays are assumed in other connections. A suitable Lyapun...
متن کاملFurther note on global exponential stability of uncertain cellular neural networks with variable delays
For cellular neural networks with time-varying delays and uncertainties, a recent work for exponential stability with convergence rate k of the networks is extended. A new linear matrix inequality criterion for the stability, which give information on the delay-dependent property, is derived. 2006 Elsevier Inc. All rights reserved.
متن کاملOn Global Exponential Stability of Discrete-Time Hopfield Neural Networks with Variable Delays
Global exponential stability of a class of discrete-time Hopfield neural networks with variable delays is considered. By making use of a difference inequality, a new global exponential stability result is provided. The result only requires the delay to be bounded. For this reason, the result is milder than those presented in the earlier references. Furthermore, two examples are given to show th...
متن کاملGlobal Robust Exponential Stability Analysis for Interval Neural Networks with Mixed Delays
and Applied Analysis 3 or equivalently ẋ t −Dx t Af x t Bf x t − τ t C ∫ t t−σ K t − s f x s ds J, 1.2 where x t x1 t , x2 t , . . . , xn t T ∈ R denotes the state vector associated with the neurons, D diag d1, d2, . . . , dn is a positive diagonal matrix, and A aij n×n, B bij n×n, and C cij n×n are the interconnection weight matrix and the time-varying delayed interconnection weight matrix and...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Applied Mathematics Letters
سال: 2006
ISSN: 0893-9659
DOI: 10.1016/j.aml.2006.01.005