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New global exponential stability criterion for neural networks with time-varying delay
- Source :
- 2017 36th Chinese Control Conference (CCC).
- Publication Year :
- 2017
- Publisher :
- IEEE, 2017.
-
Abstract
- This paper is concerned with global exponential stability problem for a class of neural networks with time-varying delays. An appropriate Lyaponov-krasovskii functional (LKF) is constructed firstly. The symmetric matrices involved in the LKF are not required to be all positive definite. The obtained criterion expressed in terms of linear matrix inequalities (LMIs) shows advantages over the existing ones since not only a novel LKF is constructed but also several techniques such as integral inequalities, convex combination technique, reciprocally convex technique and free matrix method are used to estimate the upper bound of the derivative of the LKF. Finally, two numerical examples are given to show the effectiveness and superiority of the obtained criterion.
- Subjects :
- 0209 industrial biotechnology
Mathematical optimization
Artificial neural network
Regular polygon
02 engineering and technology
Positive-definite matrix
Upper and lower bounds
020901 industrial engineering & automation
Exponential stability
0202 electrical engineering, electronic engineering, information engineering
Symmetric matrix
020201 artificial intelligence & image processing
Convex combination
Mathematics
Matrix method
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2017 36th Chinese Control Conference (CCC)
- Accession number :
- edsair.doi...........22e9a9a5da709efcd9c2aa594092a803