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A new neural network model for solving random interval linear programming problems
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DOI：10.1016/j.neunet.2016.12.007.png)
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Abstract
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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. (C) 2017 Elsevier Ltd. All rights reserved.
Keywords:
Random interval linear programming
Satisfactory solution
Convex second order cone programming
Neural network
Convergent
Stability
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