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dc.contributor.authorLiang, X-
dc.contributor.authorDing, Y-
dc.contributor.authorWang, Z-
dc.contributor.authorHao, K-
dc.contributor.authorHone, K-
dc.contributor.authorWang, H-
dc.date.accessioned2014-05-20T14:12:45Z-
dc.date.available2014-05-20T14:12:45Z-
dc.date.issued2014-
dc.identifier.citationIEEE Transactions on Cybernetics, 44(2), 240 - 251, 2014en_US
dc.identifier.issn2168-2267-
dc.identifier.urihttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6615923en
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/8485-
dc.descriptionThis is the author's accepted manuscript (under the provisional title "Bi-directional optimization of the melting spinning process with an immune-enhanced neural network"). The final published article is available from the link below. Copyright 2014 @ IEEE.en_US
dc.description.abstractA bidirectional optimizing approach for the melting spinning process based on an immune-enhanced neural network is proposed. The proposed bidirectional model can not only reveal the internal nonlinear relationship between the process configuration and the quality indices of the fibers as final product, but also provide a tool for engineers to develop new fiber products with expected quality specifications. A neural network is taken as the basis for the bidirectional model, and an immune component is introduced to enlarge the searching scope of the solution field so that the neural network has a larger possibility to find the appropriate and reasonable solution, and the error of prediction can therefore be eliminated. The proposed intelligent model can also help to determine what kind of process configuration should be made in order to produce satisfactory fiber products. To make the proposed model practical to the manufacturing, a software platform is developed. Simulation results show that the proposed model can eliminate the approximation error raised by the neural network-based optimizing model, which is due to the extension of focusing scope by the artificial immune mechanism. Meanwhile, the proposed model with the corresponding software can conduct optimization in two directions, namely, the process optimization and category development, and the corresponding results outperform those with an ordinary neural network-based intelligent model. It is also proved that the proposed model has the potential to act as a valuable tool from which the engineers and decision makers of the spinning process could benefit.en_US
dc.description.sponsorshipNational Nature Science Foundation of China, Ministry of Education of China, the Shanghai Committee of Science and Technology), and the Fundamental Research Funds for the Central Universities.en_US
dc.languageEnglish-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.subjectArtificial immune system (AIS)en_US
dc.subjectBidirectional optimizationen_US
dc.subjectNeural network (NN)en_US
dc.subjectSpinning processen_US
dc.titleBidirectional optimization of the melting spinning processen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1109/TSMCC.2013.2252896-
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Computer Science
Dept of Computer Science Research Papers

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