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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 739Support Vector Linearly Inseparable Algorithm and...

Support Vector Linearly Inseparable Algorithm and its Optimizing Microwave Calcining Technology of Ammonium Uranyl Carbonate

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Abstract:

Support vector machines (SVMs) are a promising type of learning machine based on structural risk minimization and statistical learning theory, which can be divided into two categories: support vector classification (SVC) machines and support vector regression machines (SVR). The basic elements and algorithms of SVC machines are discussed. As modeling and prediction methods are introduced into the experiment of microwave calcining AUC, the better prediction accuracy and the better fitting results are compare with back propagation (BP) neural network method. This is conducted to elucidate the good generalization performance of SVMs, especially good for dealing with the data of some nonlinearity.

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Periodical:

Advanced Materials Research (Volume 739)

Pages:

177-182

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.739.177

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Online since:

August 2013

Authors:

Qing Zhi Du

Keywords:

Ammonium Uranyl Carbonate, Microwave Calcination, Optimizing Technology, Support Vector Classification, Technology

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] B. Ayaz, A.N. Bilge: The possible usage of ex-ADU uranium dioxide fuel pellets with low-temperature sintering. Nucl. Mater. Vol. 280 (2000), pp.45-50.

DOI: 10.1016/s0022-3115(00)00033-7

[2] B. F. Pei, A. N. Duan: ADU technology and its effect on product characteristics. Nucl. PowerEng. Technol. Vol. 7 (1994), pp.41-50.

[3] J. H. Yang, et al.: Microwave process for sintering of uranium dioxide. Nucl. Mater. Vol. (325)2004, pp.210-216.

[4] Y. S. Kim: A thermodynamic evaluation of the U-O system from UO2 to U3O8. Nucl. Mater. Vol. 279(2000), pp.173-180.

[5] L. Yingwei et al.: Prediction model of ammonium uranyl carbonate calcination by microwave heati ng using incremental improved B-P neural network. Nucl. Eng. Des. Vol. 241(2011) p.1909-(1913).

DOI: 10.1016/j.nucengdes.2010.12.017

[6] L. Hongdong, et al.: Support vector machines and its application in chemistry. Chemometr. Intell. Lab. Vol. 95(2009), pp.188-198.

[7] V. Vapnik: The Nature of Statistical Learning Theory. 2rd. (Springer Publications, New York 1999).

[8] V. Vapnik: Statistical Learning Theory. (Wiley Publication, New York 1998).

[9] C. Cortes, V. Vapnik: Mach. Learn. Vol. 20 (1995), pp.273-97.

[10] N. Cristianini and J. Shawe-Taylor: An Introduction to Support Vector Machines. (: Cambridge University Press Publications, Cambridge 2000).

[11] Y. Biao, L. Wei, L. Lijun, P. Jinhui et al.: Support vector machine and its predicting stability of partially stabilized zirconia by microwave heating preparation. In: Advance Materials Research, Vol. 382(2012) pp.281-288.

DOI: 10.4028/www.scientific.net/amr.382.281

[12] U. Thissen, R.V. Brakel, A.P. de Weijer, W.J. Melssen and L.M.C. Buydens: Using support vector machines for time series prediction. Chemometr. Intell. Lab. Vol. 69(2003), pp.35-49.

DOI: 10.1016/s0169-7439(03)00111-4

[13] A.C. Lorena, A.C.P.L.F. de Carvalho: Protein cellular localization prediction with Support Vector Machines and Decision Trees. Computer Biology Medicine. Vol. 37(2007), pp.115-125.

DOI: 10.1016/j.compbiomed.2006.01.003

[14] G. Corani and M. Gatto: Structural risk minimization: a robust method for density-dependence detection and model selection. J. Ecography. Vol. 30(2007), pp.400-416.

DOI: 10.1111/j.0906-7590.2007.04863.x

[15] Y. Biao, P. Jinhui et al.: Acid-pickling plates and strips speed control system by microwave heating based on self-adaptive fuzzy PID algorithm. J. Cent. South Univ. Vol. 19(2012), pp.2179-2186.

DOI: 10.1007/s11771-012-1262-4

[16] J. H. Hong, J. K. Min, et al.: Fingerprint classification using one-vs-all support vector machines dynamically ordered with naїve Bayes classifiers. J. Pattern Recogn. Vol. 41(2008), pp.662-671.

DOI: 10.1016/j.patcog.2007.07.004

[17] Chang C.C., Lin C.J., 2001. LIBSVM-A Library for Support Vector Machines. Software. Information on http: /www. csie. ntu. tw/~cjlin/libsvm.

[18] ECRG-Toolbox, 2011. Information on http: /www. shef. ac. uk/acse/research/ecrg/getgat. html.

[19] Y. Biao, P. Jinhui, S. Hezhou et al.: Optimizing PID controller based on Genetic algorithm for industrial microwave heating device [J]. JDCTA: International Journal of Digital Content Technology and its applications. Vol. 6(2012), pp.475-482.

DOI: 10.4156/jdcta.vol6.issue23.54