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 - 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 -