A one-layer discrete-time projection neural network for support vector classification

This paper presents a one-layer discrete-time projection neural network described by difference equations for real-time support vector classification (SVC). The SVC is first formulated as a convex quadratic programming problem, and then a recurrent neural network with one-layer structure is designed...

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Veröffentlicht in:2014 International Joint Conference on Neural Networks (IJCNN) S. 3143 - 3148
Hauptverfasser: Wei Zhang, Qingshan Liu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.07.2014
Schlagworte:
Educational institutions
Optimization
Recurrent neural networks
Static VAr compensators
Support vector machines
Transient analysis
ISSN:2161-4393
Online-Zugang:Volltext
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Zusammenfassung:This paper presents a one-layer discrete-time projection neural network described by difference equations for real-time support vector classification (SVC). The SVC is first formulated as a convex quadratic programming problem, and then a recurrent neural network with one-layer structure is designed for training the support vector machine. Furthermore, simulation results on two illustrative examples are given to demonstrate the effectiveness and performance of the proposed neural network.
ISSN:2161-4393
DOI:10.1109/IJCNN.2014.6889398