Applying expert systems to hop extraction monitoring and prediction

Grzymala-Busse, J.W.; Hippe, Z.S.; Rój, E.; Skowroński, B.

Artykuł - szczegóły

Tytuł artykułu

Applying expert systems to hop extraction monitoring and prediction

Autorzy

Grzymala-Busse J.W. , Hippe Z.S. , Rój E. , Skowroński B.

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Języki publikacji

Abstrakty

Expert system technology is a major breakthrough of AI (artificial intelligence). This technology is especially useful when data describing the process are available for the data mining (knowledge acquisition). In this paper we present the rule set induction from a data set describing the process of hop extraction. Furthermore, we describe the methodology of using these rules to predict the process behavior for new, unseen situations. Thus, the expert system, containing such rule sets, may be used for monitoring the process and prediction. Two data mining methods were used for data mining: a LERS rule induction method using the MLEM2 algorithm and the generation of rule sets based on belief networks.

Słowa kluczowe

expert systems supercritical extraction hop extracts

system ekspertowy ekstrakcja nadkrytyczna

Wydawca

West Pomeranian University of Technology. Publishing House

Czasopismo

Polish Journal of Chemical Technology

Rocznik

2006

Tom

Vol. 8, nr 4

Strony

1--3

Opis fizyczny

Bibliogr. 12 poz.

Twórcy

autor

Grzymala-Busse J.W.

autor

Hippe Z.S.

autor

Rój E.

autor

Skowroński B.

lnstitute of Computer Science Polish Academy of Sciences, 01-237 Warszawa

Bibliografia

(1) Booker L. B., Goldberg D. E., Holland J. F.: Classifier systems and genetic algorithms, In Machine Learning. Paradigms and Methods. Carbonell, J. G. (ed.), The MIT Press, 1990, 235.
(2) Grzymala-Busse J. W.: Managing Uncertainty in Expert Systems. Editor: Kluwer Academic Publishers, Boston/Dordrecht/ London, 1991. Vol. 143, 224, The Kluwer International Series in Engineering and Computer Science.
(3) Grzymala-Busse J. W.: LERS-A system for learning from examples based on rough sets. In Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory ed. by R. Slowinski, Kluwer Academic Publishers, Dordrecht, Boston, London, 1992, 3.
(4) Grzymala-Busse J. W.: A new version of the rule induction system LERS, Fundamenta Informaticae, 1997, 31, 27.
(5) Grzymala-Busse J. W., Hippe Z. S., Mroczek T., Rój E., Skowroński B.: Data mining analysis of granular bed caking during hop extraction, Proceedings, 5th International Conference on Intelligent Systems Design and Applications, IEEE Computer Society, Wrocław, Poland, September 8 - 10, 2005, 426.
(6) Grzymala-Busse J. W., Hippe Z. S., Mroczek T., Rój E., Skowroński B.: Data mining experiments on hop processing data, Fifth International Conference on Hybrid Intelligent Systems, IEEE Computer Society, Rio de Janeiro, Brazil, 6-9 November, 2005, 175.
(7) Heckerman D.: A Tutorial on Learning Bayesian Networks. Technical report MSR-TR-95-06. heckerman@microsoft.com.
(8) Holland J. H., Holyoak K. J., Nisbett R. E.: Induction. Processes of Inference, Learning, and Discovery. Cambridge, MA: The MIT Press, 1986.
(9) Mroczek T., Grzymala-Busse J. W., Hippe Z. S.: Rules from belief networks: A Rough Set Approach. In: Rough Sets and Current Trends in Computing, ed. by S. Tsumoto, Slowinski, R., Komorowski, J. and Grzymala-Busse, J. W., Springer-Verlag, Uppsala, Sweden 2004, 483.
(10) Pawlak Z.: Rough Sets. International Journal of Computer and Information Sciences, 1982, 11, 341.
(11) Pawlak Z., Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht, Boston, London, 1991.
(12) Skowroński B., Mordecka Z.: Polish plant for supercritical extraction of hop (in Polish), Przem. Chem. 80, (11), 2001, 521.

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Bibliografia

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