A proximal neurodynamic model for solving inverse mixed variational inequalities. (June 2021)

• Record Type: Journal Article
• Authors: Ju, Xingxing; Li, Chuandong; He, Xing; Feng, Gang
• Is Part Of: Neural networks. Volume 138(2021)
• Page Start: 1
• Page End: 9
• Publication Date: 2021-06
• Subjects: Proximal neurodynamic model -- Inverse mixed variational inequalities -- Lipschitz continuous -- Strong monotonicity -- Exponential stability; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2021.01.012
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: 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.