Journal
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
Volume 352, Issue 3, Pages 1284-1295Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jfranklin.2014.12.023
Keywords
-
Categories
Funding
- National Natural Science Foundation of China [61304064, 61273157]
- Basic Science Research Program through National Research Foundation of Korea (NRF) - Ministry of Education, Science and Technology [2013R1A1A2A10005201]
- National Research Foundation of Korea [2013R1A1A2A10005201] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Ask authors/readers for more resources
This paper focuses on the problems of stability and dissipativity analysis for static neural networks (NN) with interval time-varying delay. A new augmented Lyapunov Krasovskii functional is firstly constructed, in which the information on the activation function is taken fully into account. Then, by employing a Wirtinger-based inequality to estimate the derivative of Lyapunov Krasovskii functional, an improved stability criterion is derived for the considered neural networks. The result is extended to dissipafivity analysis and a sufficient condition is established to assure the neural networks strictly dissipative. Two numerical examples are provided to demonstrate the effectiveness and the advantages of the proposed method. (C) 2015 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available