An inertial projection neural network for solving inverse variational inequalities

Xingxing Ju; Chuandong Li; Xing He; Gang Feng

doi:10.1016/j.neucom.2020.04.023

An inertial projection neural network for solving inverse variational inequalities

Xingxing Ju, Chuandong Li^*, Xing He, Gang Feng

^*Corresponding author for this work

Department of Biomedical Engineering

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

12 Citations (Scopus)

Abstract

A novel inertial projection neural network (IPNN) is proposed for solving inverse variational inequalities (IVIs) in this paper. It is shown that the IPNN has a unique solution under the condition of Lipschitz continuity and that the solution trajectories of the IPNN converge to the equilibrium solution asymptotically if the corresponding operator is co-coercive. Finally, several examples are presented to illustrtae the effectiveness of the proposed IPNN.

Original language	English
Pages (from-to)	99-105
Journal	Neurocomputing
Volume	406
Online published	20 Apr 2020
DOIs	https://doi.org/10.1016/j.neucom.2020.04.023
Publication status	Published - 17 Sept 2020

Research Keywords

Inertial projection neural networks
Inverse variational inequalities
Convergence analysis

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title = "An inertial projection neural network for solving inverse variational inequalities",

abstract = "A novel inertial projection neural network (IPNN) is proposed for solving inverse variational inequalities (IVIs) in this paper. It is shown that the IPNN has a unique solution under the condition of Lipschitz continuity and that the solution trajectories of the IPNN converge to the equilibrium solution asymptotically if the corresponding operator is co-coercive. Finally, several examples are presented to illustrtae the effectiveness of the proposed IPNN.",

keywords = "Inertial projection neural networks, Inverse variational inequalities, Convergence analysis",

author = "Xingxing Ju and Chuandong Li and Xing He and Gang Feng",

year = "2020",

month = sep,

day = "17",

doi = "10.1016/j.neucom.2020.04.023",

language = "English",

volume = "406",

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journal = "Neurocomputing",

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TY - JOUR

T1 - An inertial projection neural network for solving inverse variational inequalities

AU - Ju, Xingxing

AU - Li, Chuandong

AU - He, Xing

AU - Feng, Gang

PY - 2020/9/17

Y1 - 2020/9/17

N2 - A novel inertial projection neural network (IPNN) is proposed for solving inverse variational inequalities (IVIs) in this paper. It is shown that the IPNN has a unique solution under the condition of Lipschitz continuity and that the solution trajectories of the IPNN converge to the equilibrium solution asymptotically if the corresponding operator is co-coercive. Finally, several examples are presented to illustrtae the effectiveness of the proposed IPNN.

AB - A novel inertial projection neural network (IPNN) is proposed for solving inverse variational inequalities (IVIs) in this paper. It is shown that the IPNN has a unique solution under the condition of Lipschitz continuity and that the solution trajectories of the IPNN converge to the equilibrium solution asymptotically if the corresponding operator is co-coercive. Finally, several examples are presented to illustrtae the effectiveness of the proposed IPNN.

KW - Inertial projection neural networks

KW - Inverse variational inequalities

KW - Convergence analysis

UR - http://www.scopus.com/inward/record.url?scp=85084212773&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85084212773&origin=recordpage

U2 - 10.1016/j.neucom.2020.04.023

DO - 10.1016/j.neucom.2020.04.023

M3 - RGC 21 - Publication in refereed journal

SN - 0925-2312

VL - 406

SP - 99

EP - 105

JO - Neurocomputing

JF - Neurocomputing

ER -

An inertial projection neural network for solving inverse variational inequalities

Abstract

Research Keywords

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