title: A new neural network model for solving random interval linear programming problems.
abstract: This paper presents a neural network model for solving random interval linear programming problems. The original problem involving random interval variable coefficients is first transformed into an equivalent convex second order cone programming problem. A neural network model is then constructed for solving the obtained convex second order cone problem. Employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact satisfactory solution of the original problem. Several illustrative examples are solved in support of this technique.
authors.affiliation: Department of Mathematics, Semnan University, Semnan, Iran. Electronic address: z.arjmand@students.semnan.ac.ir.
authors.forename: Ziba
authors.initials: Z
authors.lastname: Arjmandzadeh
authors.affiliation: Department of Mathematics, Semnan University, Semnan, Iran. Electronic address: msafi@semnan.ac.ir.
authors.forename: Mohammadreza
authors.initials: M
authors.lastname: Safi
authors.affiliation: Department of Mathematics, Shahrood University of Technology, P.O. Box 3619995161-316, Shahrood, Iran. Electronic address: nazemi20042003@gmail.com.
authors.forename: Alireza
authors.initials: A
authors.lastname: Nazemi
mesh_terms: D006801:Humans; D016571:Neural Networks, Computer; D011340:Problem Solving; D011382:Programming, Linear / Q000639:trends; D011897:Random Allocation; D012677:Sensation
keywords: Convergent; Convex second order cone programming; Neural network; Random interval linear programming; Satisfactory solution; Stability