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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Vydáno v:2014 International Joint Conference on Neural Networks (IJCNN) s. 3143 - 3148
Hlavní autoři: Wei Zhang, Qingshan Liu
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2014
Témata:
Educational institutions
Optimization
Recurrent neural networks
Static VAr compensators
Support vector machines
Transient analysis
ISSN:2161-4393
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Shrnutí: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