MATEC Web of Conferences
Volume 44, 20162016 International Conference on Electronic, Information and Computer Engineering
|Number of page(s)||5|
|Section||Computer, Algorithm, Control and Application Engineering|
|Published online||08 March 2016|
- C.A. Van Luttervelt, and J. Peng, “Symbiosis of modelling and sensing to improve the accuracy of workpieces in small batch machining operations,” International Journal of Advanced Manufacturing Technology, 1999, vol. 15, no. 10, pp. 699–710. [CrossRef] [Google Scholar]
- D.M. Hawkins, and K.D. Zamba, “On Small Shifts in Quality Control,” Quality Engineering, Sep. 2003, vol. 16, no. 1, pp. 143–149. [CrossRef] [Google Scholar]
- H.A. Kishawy, “An experimental evaluation of cutting temperatures during high speed machining of hardened D2 tool steel”, Machining Science and Technology, 2002, vol. 6, no. 1, pp. 67–79. [CrossRef] [Google Scholar]
- E.S. Chng, S. Chen, and B. Mulgrew, “Gradient radial basis function networks for nonlinear and nonstationary time series prediction,” IEEE Transactions on Neural Networks, vol. 7, no.1, pp. 190–194, 1996. [CrossRef] [Google Scholar]
- Y. Becerikli, and Y. Oysal, “Modeling and prediction with a class of time delay dynamic neural network,” Applied Soft Computing Journal, Aug. 2007, vol. 7, no. 4, pp. 1164–1169. [CrossRef] [Google Scholar]
- E. Gomez-Ramirez, and K. Najim, E. Ikonen, “Forecasting time series with a new architecture for polynomial artificial neural network,” Applied Soft Computing Journal, Aug. 2007, vol. 7, no. 4, pp. 1209–1216. [CrossRef] [Google Scholar]
- V.N. Vapnik, The nature of statistical learning theory. New York: Spring-Verlag, 1999. [Google Scholar]
- V.N. Vapnik, “An overview of statistical learning theory,” IEEE Transaction Neural Networks, 1999, vol. 10, no.5, pp. 988–999. [Google Scholar]
- J. A. K. Suykens, and J. Vandewalle, “Least squares support vector machine classifiers,” Neural Processing Letters, 1999, vol. 9, no. 3, pp. 293–300. [Google Scholar]
- J. A. K. Suykens, J. Vandewalle, “Sparse least squares support vector machine classifiers,” European Symposium on Artificial Neural Networks, 2000, Bruges Belgium, 37–42. [Google Scholar]
- J.A.K. Suykens, L. Lukas, and J. Vandewalle, “Sparse approximation using least squares support vector machines,” Proceedings - IEEE International Symposium on Circuits and Systems, Geneva, vol. 2, pp. 757–760, 2000. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.