title: A recurrent neural network based on projection operator for extended general variational inequalities.
abstract: Based on the projection operator, a recurrent neural network is proposed for solving extended general variational inequalities (EGVIs). Sufficient conditions are provided to ensure the global convergence of the proposed neural network based on Lyapunov methods. Compared with the existing neural networks for variational inequalities, the proposed neural network is a modified version of the general projection neural network existing in the literature and capable of solving the EGVI problems. In addition, simulation results on numerical examples show the effectiveness and performance of the proposed neural network.
authors.affiliation: School of Automation, Southeast University, Nanjing 210096, China. qsliu@seu.edu.cn
authors.forename: Qingshan
authors.initials: Q
authors.lastname: Liu
authors.forename: Jinde
authors.initials: J
authors.lastname: Cao
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D008962:Models, Theoretical; D016571:Neural Networks, Computer