TY - JOUR
T1 - Exponential stability criteria for linear neutral systems with applications to neural networks of neutral type
AU - Berezansky, Leonid
AU - Diblík, Josef
AU - Svoboda, Zdeněk
AU - Šmarda, Zdeněk
N1 - Funding Information:
The second, third and fourth authors were supported by the project of specific university research at Brno University of Technology FEKT-S-20-6225. The authors would like to thank the editor and the reviewers for their detailed comments and valuable suggestions.
Publisher Copyright:
© 2022 The Franklin Institute
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Linear neutral vector equations x˙(t)=A0(t)x˙(h0(t))+∑k=1mAk(t)x(hk(t))+∫g(t)tP(t,s)x(s)dsare considered on interval [0,∞). Here x=(x1,…,xn)T, m is a positive integer, the entries of matrices Al, l=0,…,m, P, and the delays hk, k=0,…,m, g are assumed to be Lebesgue measurable functions. New explicit criteria are derived on uniform exponential stability. Comparisons are made and discussed based on an overview of the existing results. An application is presented to local exponential stability of non-autonomous neural network models of neutral type.
AB - Linear neutral vector equations x˙(t)=A0(t)x˙(h0(t))+∑k=1mAk(t)x(hk(t))+∫g(t)tP(t,s)x(s)dsare considered on interval [0,∞). Here x=(x1,…,xn)T, m is a positive integer, the entries of matrices Al, l=0,…,m, P, and the delays hk, k=0,…,m, g are assumed to be Lebesgue measurable functions. New explicit criteria are derived on uniform exponential stability. Comparisons are made and discussed based on an overview of the existing results. An application is presented to local exponential stability of non-autonomous neural network models of neutral type.
UR - http://www.scopus.com/inward/record.url?scp=85143544267&partnerID=8YFLogxK
U2 - 10.1016/j.jfranklin.2022.11.012
DO - 10.1016/j.jfranklin.2022.11.012
M3 - Article
AN - SCOPUS:85143544267
SN - 0016-0032
VL - 360
SP - 301
EP - 326
JO - Journal of the Franklin Institute
JF - Journal of the Franklin Institute
IS - 1
ER -