TY - JOUR
T1 - A proximal neurodynamic model for solving inverse mixed variational inequalities
AU - Ju, Xingxing
AU - Li, Chuandong
AU - He, Xing
AU - Feng, Gang
PY - 2021/6
Y1 - 2021/6
N2 - This paper proposes a proximal neurodynamic model (PNDM) for solving inverse mixed variational inequalities (IMVIs) based on the proximal operator. It is shown that the PNDM has a unique continuous solution under the condition of Lipschitz continuity (L-continuity). It is also shown that the equilibrium point of the proposed PNDM is asymptotically stable or exponentially stable under some mild conditions. Finally, three numerical examples are presented to illustrate effectiveness of the proposed PNDM.
AB - This paper proposes a proximal neurodynamic model (PNDM) for solving inverse mixed variational inequalities (IMVIs) based on the proximal operator. It is shown that the PNDM has a unique continuous solution under the condition of Lipschitz continuity (L-continuity). It is also shown that the equilibrium point of the proposed PNDM is asymptotically stable or exponentially stable under some mild conditions. Finally, three numerical examples are presented to illustrate effectiveness of the proposed PNDM.
KW - Exponential stability
KW - Inverse mixed variational inequalities
KW - Lipschitz continuous
KW - Proximal neurodynamic model
KW - Strong monotonicity
UR - http://www.scopus.com/inward/record.url?scp=85101345566&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85101345566&origin=recordpage
U2 - 10.1016/j.neunet.2021.01.012
DO - 10.1016/j.neunet.2021.01.012
M3 - RGC 21 - Publication in refereed journal
SN - 0893-6080
VL - 138
SP - 1
EP - 9
JO - Neural Networks
JF - Neural Networks
ER -