Global Robust Exponential Stability Analysis for Interval Neural Networks with Mixed Delays
A class of interval neural networks with time-varying delays and distributed delays is investigated. By employing H-matrix and M-matrix theory, homeomorphism techniques, Lyapunov functional method, and linear matrix inequality approach, sufficient conditions for the existence, uniqueness, and global...
Saved in:
Main Authors: | Yanke Du, Rui Xu |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2012-01-01
|
Series: | Abstract and Applied Analysis |
Online Access: | http://dx.doi.org/10.1155/2012/647231 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Global Robust Exponential Stability and Periodic Solutions for Interval Cohen-Grossberg Neural Networks with Mixed Delays
by: Yanke Du, et al.
Published: (2013-01-01) -
Global Exponential Robust Stability of Static Interval Neural Networks with Time Delay in the Leakage Term
by: Guiying Chen, et al.
Published: (2014-01-01) -
Global Robust Exponential Dissipativity for Interval Recurrent Neural Networks with Infinity Distributed Delays
by: Xiaohong Wang, et al.
Published: (2013-01-01) -
Switched Exponential State Estimation and Robust Stability for Interval Neural Networks with Discrete and Distributed Time Delays
by: Hongwen Xu, et al.
Published: (2012-01-01) -
Global Exponential Stability of Antiperiodic Solutions for Discrete-Time Neural Networks with Mixed Delays and Impulses
by: Xiaofeng Chen, et al.
Published: (2012-01-01)