A neural network model for solving convex quadratic programming problems with some applications

This paper presents a capable neural network for solving strictly convex quadratic programming (SCQP) problems with general linear constraints. The proposed neural network model is stable in the sense of Lyapunov and can converge to an exact optimal solution of the original problem. A block diagram...

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Bibliographic Details
Published inEngineering applications of artificial intelligence Vol. 32; pp. 54 - 62
Main Author Nazemi, Alireza
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.06.2014
Subjects
Artificial intelligence
Block diagrams
Convergent
Dynamic model
Expert systems
Mathematical models
Neural network
Neural networks
Optimality conditions
Optimization
Quadratic programming
Stability
Convergent
Optimality conditions
Stability
Neural network
Quadratic programming
Dynamic model
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Summary:This paper presents a capable neural network for solving strictly convex quadratic programming (SCQP) problems with general linear constraints. The proposed neural network model is stable in the sense of Lyapunov and can converge to an exact optimal solution of the original problem. A block diagram of the proposed model is also given. Several applicable examples further show the correctness of the results and the good performance of the model.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2014.02.014