Abstract
Large industrial complexes with hundreds of variables must be tightly monitored for safety, quality and resources optimization. Multidimensional scaling and computational intelligence are proposed in this work as effective tools for building classifiers of the operating state of the industrial process into normal / abnormal working regions. The VisRed, Visualization by Data Reduction computational framework, is extended with techniques from computational intelligence, such as neural networks (several architectures), support vector machines and neuro-fuzzy systems (in an evolving adaptive implementation) to build such classifiers. The Visbreaker plant of an oil refinery is taken as case study and some scenarios show the potentiality of the combined approach.
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References
Borg, I., Groenen, P.: Modern Multidimensional Scaling, Theory and Applications, 2nd edn. Springer, Heidelberg (2005)
de Oliveira, M.C.F., Levkowitz, H.: From visual data exploration to visual data mining: A survey. IEEE Trans on Visualization and Computer Graphics 9(3), 378–394 (2003)
Dourado, A., Ferreira, E., Barbeiro, P.: VISRED – Numerical Data Mining with Linear and Nonlinear Techniques. In: Perner, P. (ed.) ICDM 2007. LNCS, vol. 4597, pp. 92–106. Springer, Heidelberg (2007)
Dourado, A., Ferreira, E., Barbeiro, P.: VisRedII-Introducing Meteheuristics in Multidimensional Scaling, Short Paper. In: Perner, P. (ed.) ICDM 2008. LNCS, vol. 5077. Springer, Heidelberg (2008)
The Mathworks, Inc., http://www.mathworks.com/
van der Maaten, L.J.P.: An Introduction to Dimensionality Reduction Using Matlab. Technical Report MICC 07-07. Maastricht University, Maastricht, The Netherlands (2007), http://ticc.uvt.nl/~lvdrmaaten/Laurens_van_der_Maaten/Matlab_Toolbox_for_Dimensionality_Reduction_files/Report_final.pdf
Tenenbaum, J.B., de Silva, V., Langford, J.C.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290(5500), 2319–2323 (2000)
Agrafiotis, D.K.: Stochastic Proximity Embedding,, Int. J. Comput. Chem. 24: 1215–1221, Wiley Periodicals (2003)
Hill, T., Lewicki, P.: STATISTICS Methods and Applications. StatSoft, Tulsa, OK (Printed Version) (2007) Electronic textbook, http://www.statsoft.com/textbook/stfacan.html#index
Geoffrey Hinton and Sam Roweis Department of Computer Science, University of Toronto 10 King’s College Road, Toronto, M5S 3G5 Canada fhinton,roweisg@cs.toronto.edu
Belkin, M., Niyogi, P.: Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Computation 15(6), 1373–1396 (2003)
Widrow, B., Sterns, S.D.: Adaptive Signal Processing. Prentice-Hall, New York (1985)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Rumelhart, D.E., McClelland, J. (eds.) Parallel Data Processing, vol. 1, ch. 8, pp. 318–362. MIT Press, Cambridge (1986)
Hagan, M.T., Demuth, H.B., Beale, M.: Neural Network Design. PWS Publishing (1995)
Chen, S., Cowan, C.F.N., Grant, P.M.: Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks. IEEE Transactions on Neural Networks 2(2), 302–309 (1991)
Vapnik, V.: Statistical Learning Theory. Wiley Interscience, New York (1998)
Briggs, Tom, http://webspace.ship.edu/thbrig/mexsvm/
Joachims, Thorsten, http://svmlight.joachims.org/
Chen, P.H., Lin, C.J., Schölkopf, B.: A tutorial on ν-support vector machines. Appl. Stoch. Models. Bus. Ind. 21, 111–136 (2005)
Ross, T.: Fuzzy Logic With Engineering Applications, 2nd edn. McGraw Hill, New York (2004)
Angelov, P., Filev, D.: An Approach to Online Identification of Takagi-Sugeno Fuzzy Models. IEEE Transactions on Systems, Man, and Cybernetics - Part B 34 (2004)
Victor, J., Dourado, A.: Evolving Takagi-Sugeno fuzzy models. Technical Report, CISUC (September 2003), http://cisuc.dei.uc.pt/acg/view_pub.php?id_p=760
Ramos, J.V., Dourado, A.: On line interpretability by rule base simplification and reduction. In: Eunite Symposium, Aachen (2004)
Galp data, Sines Refinery (2006)
Andrews, D.F.: Plots of high dimensional data. Biometrics 28, 125–136 (1972)
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Dourado, A., Silva, S., Aires, L., Araújo, J. (2009). Combining Multidimensional Scaling and Computational Intelligence for Industrial Monitoring. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2009. Lecture Notes in Computer Science(), vol 5633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03067-3_19
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DOI: https://doi.org/10.1007/978-3-642-03067-3_19
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