**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**30835

##### Performance Comparison of Different Regression Methods for a Polymerization Process with Adaptive Sampling

**Authors:**
Florin Leon,
Silvia Curteanu

**Abstract:**

**Keywords:**
Machine Learning,
batch bulk methyl methacrylate polymerization,
adaptive sampling,
large margin nearest neighbor regression

**Digital Object Identifier (DOI):**
doi.org/10.5281/zenodo.1126728

**References:**

[1] S. Curteanu, “Direct and Inverse Neural Network Modeling in Free Radical Polymerization”, Central European Journal of Chemistry, vol. 2, no. 1, 2004, pp. 113–140.

[2] S. Curteanu, F. Leon and D. Gâlea, “Neural Network Models for Free Radical Polymerization of Methyl Methacrylate”, Eurasian Chemico-Technological Journal, vol. 5, no. 3, 2003, pp. 225–231.

[3] J. C. B. Gonzaga, L. A. C. Meleiro, C. Kiang and R. Maciel Filho, “ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process”, Computers & Chemical Engineering, vol. 33, no. 1, 2009, pp. 43–49.

[4] S. Contant, P. V. R. Mesa and L. M. F. Lona, “Modeling of Styrene Living Radical Polymerization Mediated by Tempo Using Neural Networks”, Proceedings of the 2nd Mercosur Congress on Chemical Engineering and 4th Mercosur Congress on Process System Engineering, ENPROMER, 2005, Rio de Janeiro, Brazil, 2005, pp. 1–10.

[5] F. A. N. Fernandes, “Selection of a mixture of initiators for batch polymerization using neural networks”, Journal of Applied Polymer Science, vol. 98, issue 5, 2005, pp. 2088–2093.

[6] S. Curteanu, F. Leon, R. Furtună, E. N. Drăgoi and N. Curteanu, “Comparison between Different Methods for Developing Neural Network Topology Applied to a Complex Polymerization Process”, Proceedings of the International Joint Conference on Neural Networks, IEEE World Congress on Computational Intelligence, Barcelona, Spain, 2010, pp. 1293–1300.

[7] M. S. Leite, B. F. Dos Santos, L. M. F. Lona, F. V. Da Silva and A. M. Frattini Fileti, “Application of Artificial Intelligence Techniques for Temperature Prediction in a Polymerization Process”, Chemical Engineering Transactions, vol. 24, 2011, pp. 385–390.

[8] A. Salman, A. P. Engelbrecht and M. G. H. Omran, “Empirical analysis of self-adaptive differential evolution”, European Journal of Operational Research, vol. 183, issue 2, 2007, pp. 785–804.

[9] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten, “The WEKA Data Mining Software: An Update”, ACM SIGKDD Explorations, vol. 11, no. 1, 2009, pp. 10–18.

[10] F. Leon and S. Curteanu, “Evolutionary Algorithm for Large Margin Nearest Neighbour Regression”, Proceedings of the 7th International Conference on Computational Collective Intelligence Technologies and Applications, ICCCI 2015, Madrid, Spain, Part I, Lecture Notes in Artificial Intelligence, LNAI 9329, Springer International Publishing Switzerland, doi: 10.1007/978-3-319-24069-5_29, 2015, pp. 286–296.

[11] F. Leon and S. Curteanu, “Large Margin Nearest Neighbour Regression Using Different Optimization Techniques”, Journal of Intelligent and Fuzzy Systems, IOS Press, in press

[12] K. Q. Weinberger, J. Blitzer and L. K. Saul, “Distance metric learning for large margin nearest neighbor classification”, Advances in Neural Information Processing Systems, vol. 18, MIT Press, Cambridge, MA, USA, 2006, pp. 1473–1480.

[13] K. Q. Weinberger and L. K. Saul, “Fast solvers and efficient implementations for distance metric learning”, Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland, 2008, pp. 1160–1167.

[14] K. Q. Weinberger and L. K. Saul, “Distance Metric Learning for Large Margin Nearest Neighbor Classification”, Journal of Machine Learning Research, vol. 10, 2009, pp. 207–244.

[15] H. Jeffreys and B. S. Jeffreys, “Central Differences Formula”, in Methods of Mathematical Physics, 3rd edition, Cambridge University Press, 1988, pp. 284–286.