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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 6/2017

08.08.2016 | Original Article

A projected-based neural network method for second-order cone programming

verfasst von: Yaling Zhang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 6/2017

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Abstract

A projected-based neural network method for second-order cone programming is proposed. The second-order cone programming is transformed into an equivalent projection equation. The projection on the second-order cone is simple and costs less computation time. We prove that the proposed neural network is stable in the sense of Lyapunov and converges to an exact solution of the second-order cone programming problem. The simulation experiments show our method is an efficient method for second-order cone programming problems.
Vorheriger Artikel A segmentation-based adaptive image enhancement method inspired by the self-adjust features of HVS
Nächster Artikel Pseudo almost periodic solutions for neutral type high-order Hopfield neural networks with mixed time-varying delays and leakage delays on time scales

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Metadaten
Titel
A projected-based neural network method for second-order cone programming
verfasst von
Yaling Zhang
Publikationsdatum
08.08.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 6/2017
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-016-0569-0

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