title: Neural network for solving convex quadratic bilevel programming problems.
abstract: In this paper, using the idea of successive approximation, we propose a neural network to solve convex quadratic bilevel programming problems (CQBPPs), which is modeled by a nonautonomous differential inclusion. Different from the existing neural network for CQBPP, the model has the least number of state variables and simple structure. Based on the theory of nonsmooth analysis, differential inclusions and Lyapunov-like method, the limit equilibrium points sequence of the proposed neural networks can approximately converge to an optimal solution of CQBPP under certain conditions. Finally, simulation results on two numerical examples and the portfolio selection problem show the effectiveness and performance of the proposed neural network.
authors: School of Electronics and Information Engineering, Southwest University, Chongqing 400715, PR China. Electronic address: hexingdoc@hotmail.com.
authors: Xing
authors: X
authors: He
authors: School of Electronics and Information Engineering, Southwest University, Chongqing 400715, PR China. Electronic address: licd@cqu.edu.cn.
authors: Chuandong
authors: C
authors: Li
authors: Texas A & M University at Qatar, Doha 5825, Qatar. Electronic address: tingwen.huang@qatar.tamu.edu.
authors: Tingwen
authors: T
authors: Huang
authors: School of Science, Information Technology and Engineering, University of Ballarat, Mt Helen, VIC 3350, Australia. Electronic address: cjlee.cqu@163.com.
authors: Chaojie
authors: C
authors: Li
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016571:Neural Networks, Computer; D012984:Software
keywords: Convex quadratic bilevel programming problems; Neural network; Nonautonomous differential inclusions; Nonsmooth analysis