TY - JOUR
T1 - Finite-time stability analysis for fractional-order Cohen–Grossberg BAM neural networks with time delays
AU - Rajivganthi, C.
AU - Rihan, F. A.
AU - Lakshmanan, S.
AU - Muthukumar, P.
N1 - Funding Information:
The support of the UAE University, through UPAR and center-based research projects, to execute this work is highly acknowledged and appreciated. The authors would like to thank the editor and reviewers for their constructive comments and suggestions which improved the manuscript.
Publisher Copyright:
© 2016, The Natural Computing Applications Forum.
PY - 2018/6/1
Y1 - 2018/6/1
N2 - In this paper, the problem of finite-time stability for a class of fractional-order Cohen–Grossberg BAM neural networks with time delays is investigated. Using some inequality techniques, differential mean value theorem and contraction mapping principle, sufficient conditions are presented to ensure the finite-time stability of such fractional-order neural models. Finally, a numerical example and simulations are provided to demonstrate the effectiveness of the derived theoretical results.
AB - In this paper, the problem of finite-time stability for a class of fractional-order Cohen–Grossberg BAM neural networks with time delays is investigated. Using some inequality techniques, differential mean value theorem and contraction mapping principle, sufficient conditions are presented to ensure the finite-time stability of such fractional-order neural models. Finally, a numerical example and simulations are provided to demonstrate the effectiveness of the derived theoretical results.
KW - Banach contraction principle
KW - Cohen–Grossberg BAM neural networks
KW - Finite-time stability
KW - Fractional-order derivative
KW - Time delay
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U2 - 10.1007/s00521-016-2641-9
DO - 10.1007/s00521-016-2641-9
M3 - Article
AN - SCOPUS:84994514552
SN - 0941-0643
VL - 29
SP - 1309
EP - 1320
JO - Neural Computing and Applications
JF - Neural Computing and Applications
IS - 12
ER -