EXPONENTIAL STABILITY ANALYSIS FOR NEURAL NETWORKS WITH TIME-VARYING DELAY AND LINEAR FRACTIONAL PERTURBATIONS

Authors

Chang-Hua Lien, Department of Marine Engineering, National Kaohsiung Marine University, Kaohsiung, Taiwan, R.O.CFollow
Ker-Wei Yu, Department of Marine Engineering, National Kaohsiung Marine University, Kaohsiung, Taiwan, R.O.C

Abstract

In this paper, the global exponential stability and global asymptotic stability for a class of uncertain delayed neural networks (UDNNs) with time-varying delay and linear fractional perturbations are considered. Delay-dependent and delay-independent criteria are proposed to guarantee the robust stability of UDNNs via linear matrix inequality (LMI) approach. Additional nonnegative inequality approach is used to improve the conservativeness of the stability criteria. Some numerical examples are illustrated to show the effectiveness of our results. From the simulation results, significant improvement over the recent results can be observed.

Recommended Citation

Lien, Chang-Hua and Yu, Ker-Wei (2014) "EXPONENTIAL STABILITY ANALYSIS FOR NEURAL NETWORKS WITH TIME-VARYING DELAY AND LINEAR FRACTIONAL PERTURBATIONS," Journal of Marine Science and Technology: Vol. 22: Iss. 2, Article 5.
DOI: 10.6119/JMST-013-0207-3
Available at: https://jmstt.ntou.edu.tw/journal/vol22/iss2/5