A new neural network model for solving random interval linear programming problems. (May 2017)

• Record Type: Journal Article
• Authors: Arjmandzadeh, Ziba; Safi, Mohammadreza; Nazemi, Alireza
• Is Part Of: Neural networks. Volume 89(2017)
• Page Start: 11
• Page End: 18
• Publication Date: 2017-05
• Subjects: Random interval linear programming -- Satisfactory solution -- Convex second order cone programming -- Neural network -- Convergent -- 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.2016.12.007
• Languages: English
• ISSNs: 0893-6080

Abstract

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.