Journal
NEURAL NETWORKS
Volume 119, Issue -, Pages 190-199Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2019.08.003
Keywords
State estimation; BAM neural networks; Memristive; Fractional-order; Non-fragile
Funding
- National Natural Science Foundation of China [61973258, 61573291, 61573096]
- Fundamental Research Funds for Central Universities [XDJK2016B036]
- National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2017R1A2B2004671]
Ask authors/readers for more resources
This paper deals with the non-fragile state estimation problem for a class of fractional-order memristive BAM neural networks (FMBAMNNs) with and without time delays for the first time. By means of a novel transformation and interval matrix approach, non-fragile estimators are designed and parameter mismatch problem is averted. Sufficient criteria are established to ascertain the error system is asymptotically stable based on fractional-order Lyapunov functionals and linear matrix inequalities (LMIs). Two examples are put forward to show the effectiveness of the obtained results. (C) 2019 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