dc.contributor.author |
Miao, Peng
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|
dc.contributor.author |
Shen, Yanjun
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|
dc.contributor.author |
Xia, Xiaohua
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dc.date.accessioned |
2014-08-06T10:11:26Z |
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dc.date.available |
2014-08-06T10:11:26Z |
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dc.date.issued |
2014-11 |
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dc.description.abstract |
In this paper, finite time dual neural networks with a new activation function are presented to solve quadratic programming problems.The activation function has two tunable parameters,which give more flexibility to design the neural networks.By Lyapunov theorem, finite-time stability can be derived for the proposed neural networks, and the actual optimal solutions of the quadratic programming problems can be obtained in finite time interval. Different f rom the existing recurrent neural networks for solving the quadratic programming problems, the neural networks of this paper have a faster convergent speed,at the same time, they can reduce oscillation when delay appears,and have less sensitivity to additive noise with careful selection of the parameters.Simulations are presented to evaluate the performance of the neural networks with the tunable activation function.In addition, the proposed neural networks are applied to estimate parameters at for an energy model of belt conveyors.The effectiveness of our methods are validated by theoretical analysis and numerical simulations. |
en_US |
dc.description.librarian |
hb2014 |
en_US |
dc.description.sponsorship |
National Science Foundation of China (61374028,61174216 and 51177088), the Grant National Science Foundation of Hubei Provincial (2013CFA050), the Scientific Innovation Team Project of Hubei Provincial Department of Education (T201103), the Graduate Scientific Research Foundation of China Three Gorges University (2014PY069). |
en_US |
dc.description.uri |
http://www.elsevier.com/locate/neucom |
en_US |
dc.identifier.citation |
Miao, P, Shen, Y & Xia, X 2014, 'Finite time dual neural networks with a tunable activation function for solving quadratic programming problems and its application', Neurocomputing, vol. 143, pp. 80-89. |
en_US |
dc.identifier.issn |
0925-2312 (print) |
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dc.identifier.issn |
1872-8286 (online) |
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dc.identifier.other |
10.1016/j.neucom.2014.06.018 |
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dc.identifier.uri |
http://hdl.handle.net/2263/41092 |
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dc.language.iso |
en |
en_US |
dc.publisher |
Elsevier |
en_US |
dc.rights |
© 2014 Elsevier B.V.All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, vol. 143, pp. 80-89, 2014. doi : 10.1016/j.neucom.2014.06.018. |
en_US |
dc.subject |
Recurrent neural networks |
en_US |
dc.subject |
Finite-time stability |
en_US |
dc.subject |
Tunable activation function |
en_US |
dc.subject |
Quadratic programming problem |
en_US |
dc.title |
Finite time dual neural networks with a tunable activation function for solving quadratic programming problems and its application |
en_US |
dc.type |
Postprint Article |
en_US |