title: A recurrent neural network for solving a class of generalized convex optimization problems.
abstract: In this paper, we propose a penalty-based recurrent neural network for solving a class of constrained optimization problems with generalized convex objective functions. The model has a simple structure described by using a differential inclusion. It is also applicable for any nonsmooth optimization problem with affine equality and convex inequality constraints, provided that the objective function is regular and pseudoconvex on feasible region of the problem. It is proven herein that the state vector of the proposed neural network globally converges to and stays thereafter in the feasible region in finite time, and converges to the optimal solution set of the problem.
authors.affiliation: Department of Mathematics, Tarbiat Modares University, P.O. Box 14115-175, Tehran, Iran. a.r_hosseini@yahoo.com
authors.forename: Alireza
authors.initials: A
authors.lastname: Hosseini
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
authors.forename: S Mohammad
authors.initials: SM
authors.lastname: Hosseini
mesh_terms: D016571:Neural Networks, Computer; D011340:Problem Solving; D013997:Time Factors