. Kadlec P., Gabrys B. and Strandt S., “Data-driven soft sensors in the process industry”, Comput. Chem. Eng., Vol. 33, pp. 795, 2009.
. Pan T .H., Wong D. S. H. and Jang S .S., “Development of a novel soft sensor using a local model network with an adaptive subtractive clustering approach”, Ind. Eng. Chem. Res., Vol. 49, pp. 4738–4747, 2010.
. Gonzagaa J. C. B., Meleirob L. A. C., Kianga C. and Filho R. M., “ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process”, Comput. Chem. Eng. Vol. 33, pp. 43–49, 2009.
. Chitralekha S. B. and Shah S.L., “Application of support vector regression for developing soft sensors for nonlinear processes”, The Canadian Journal of Chemical Engineering. Vol. 88, pp. 696-709, 2010.
. Park T. C., Kim T. Y., and Yeo Y. K., “Prediction of the melt flow index using partial least squares and support vector regression in high-density polyethylene (HDPE) process”, Korean J. Chem. Eng., 27(6), pp. 1662-1668, 2010.
. Dutta S. and Gupta J. P., “PVT correlations of indian crude using support vector regression”, Energy & Fuels, Vol. 23, pp. 5483–5490, 2009.
. Yan W., Shao H. and Wang X., “Soft sensing modeling based on support vector machine and Bayesian model selection”, Comput. Chem. Eng., Vol. 28, pp. 1489–1498, 2004.
. Desai K., Badhe Y., Tambe S. S. and Kulkarni B. D., “Soft-sensor development for fed-batch bioreactors using support vector regression”, Biochemical Engineering Journal 27, pp. 225–239, 2006.
. Guohai L., Dawei Z., Haixia X. and Congli M., “Model optimization of SVM for a fermentation soft sensor”, Expert Systems with Applications, Vol. 37, pp. 2708–2713, 2010.
. Liu Y., Hu N., Wang H. and Li P., “Soft chemical analyzer development using adaptive least-squares support vector regression with selective pruning and variable moving window size”, Ind. Eng. Chem. Res., Vol. 48, pp. 5731–574, 2009.
. Hong W. C., “Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm”, Neurocomputing, Vol. 74, 2096-2107, 2011.
. Minqiang P., Dehuai Z. and Gang X., “Temperature prediction of hydrogen producing reactor using SVM regression with PSO”, Journal of computers,Vol 5, No.3, 2010.
. Yin J., “LogP prediction for blocked tripeptides with amino acids descriptors (HMLP) by multiple linear regression and support vector regression”, Procedia Environmental Sciences 8, pp. 173–178, 2011.
. Vapnik V. N. The nature of statistical learning theory, 2nd, ed. New York: Springer; 1999.
. Boser B. E., Guyon I. M. and Vapnik V. N. A training algorithm for optimal margin classifiers, In D. Haussler, editor, 5th Annual ACM Workshop on COLT, 144-152, Pittsburgh, PA, ACM Press, 1992.
. Vapnik V. N., Statistical learning theory, Wiley, New York, 1998.
. Basak D., Pal S. and Patranabis D. C., “Support vector regression”, Neural Inf. Process. Vol. 11, pp. 203–225, 2007.
. Cherkassky V. and Ma Y., “Practical selection of SVM parameters and noise estimation for SVM regression”, Neural Networks, Vol. 17, pp. 113–126, 2004.
. Benqlilou C., Data reconciliation as a framework for chemical processes optimization and control, PHD Thesis,March, 2004.
. Singh M. K., Denising of natural images using the wavelet transform, Master's Thesis. San Jose State University, 2010.
. Huang H. P. and Luo K. Y., “On-line wavelets filtering with application to linear dynamic data reconciliation”, Ind. Eng. Chem. Res. Vol. 46, pp. 8746-8755, 2007.
. Unser M. and Blu T., “Wavelet theory demystified”, IEEE Transaction on Signal Processing, 51(2), pp. 470-483, 2003.
. Shukla P. D., Complex wavelet transforms and their applications, Master of Philosophy Thesis, University of Strathclyde , 2003.
. Phinyomark A., Limsakul C., and Phukpattaranont P., “A comparative study of wavelet denoising for multifunction myoelectric control”, inInternational Conference on Computer and AutomationEngineering, pp. 21–25, 2009.
. Jiang C. F. and Kuo S. L., “A comparative study of wavelet denoising of durface electromyographic signals”, in 29th Annual International Conferenceof the IEEE Engineering in Medicine and BiologySociety, pp. 1868–1871, 2007.
. Somasundaram K. and Vimala S., “Fast encoding algorithm for vector quantization”, International Journal of Engineering Science and Technology, Vol. 2(9), pp. 4876-4879, 2010.
. Yu T., Simoff S. and Jan T., “VQSVM: a case study for incorporating prior domain knowledge in to inductive machine learning”, Neurocomputing Vol. 73, pp. 2614–2623, 2010.
. Chang C. C. and Lin C. J., LIBSVM: a library for support vector machines, Software http://www.csie.ntu.edu.tw/~cjlin/libsvm/, 2001.