A high performance neural network model for solving chance constrained optimization problems

This paper presents a neural network model to solve chance constrained optimization (CCO) problems. The main idea is to convert the chance constrained problem into an equivalent convex second order cone programming (CSOCP) problem. A neural network model is then constructed for solving the obtained...

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Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 121; S. 540 - 550
Hauptverfasser: Nazemi, Alireza, Tahmasbi, Narges
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 09.12.2013
Elsevier
Schlagworte:
Applied sciences
Artificial intelligence
Chance constrained optimization
Computer science; control theory; systems
Computer simulation
Connectionism. Neural networks
Constraints
Convergence
Equivalence
Exact sciences and technology
Lyapunov functions
Mathematical models
Neural network models
Neural networks
Optimization
Programming
Stability
Stochastic linear programming
Chance constrained optimization
Neural network models
Stability
Stochastic linear programming
Convergence
High performance
conic programming
Lyapunov method
Linear programming
Neural network
Convex surface
Modeling
Convex programming
Constrained optimization
Stochastic programming
Exact solution
Optimal solution
Problem solving
Lyapunov function
Mathematical programming
ISSN:0925-2312, 1872-8286
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:This paper presents a neural network model to solve chance constrained optimization (CCO) problems. The main idea is to convert the chance constrained problem into an equivalent convex second order cone programming (CSOCP) problem. A neural network model is then constructed for solving the obtained CSOCP problem. By 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 optimal solution of the original problem. The simulation results also show that the proposed neural network is feasible and efficient.
Bibliographie:ObjectType-Article-1
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ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2013.05.034