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Advances in Computational Intelligence pp 46–77Cite as

Data-Based Fuzzy Modeling for Complex Applications

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Part of the book series: Natural Computing Series ((NCS))

Summary

As a consequence of the continually increasing complexity of technical systems and processes, the requirement for high-performing modeling techniques is increasing. The methods of Computational Intelligence (CI), such as fuzzy modeling or artificial neural networks, are specifically designed to deal with imprecise, incomplete, and partially incorrect information in dynamically changing environments and large variable spaces. Fuzzy modeling has the additional advantage that, with certain restrictions, interpretable models are obtained. However, especially for data-based approaches, there are still difficulties in generating efficiently interpretable rule bases with the required accuracy. Moreover, particularly with regard to complex applications, efficiency, interpretability, and accuracy are often partly contradictory modeling objectives. The main focus of this contribution is on determining an adequate compromise in this conflict area. The applicability of the concepts presented is demonstrated by means of complex real-world applications in the domains of power management control, classification in quality control, and prediction in financial service.

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References

  1. J. Albrecht. Vorausschauende optimale Steuer-und Regelstrategien zur Verbesserung der Kraftwerksführung. Fortschritt-Berichte VDI, Reihe 8, Nr. 616. VDI Verlag, Düsseldorf, 1997.

    Google Scholar 

  2. J. Albrecht, K. Albers, and P. Stelzner. Sequentielle vorausschauende Vorsteuerung eines Kraftwerksparks durch ein Führungssystem. at-Automatisierungstechnik, 44 (8): 381–390, 1996.

    Google Scholar 

  3. J. Albrecht, H. Kiendl, A. Michalske, K. Albers, and P. Stelzner. Verfahren zum Regeln der Leistung eines Kraftwerksparks, 1995. Patent, DE 195 10 342.

    Google Scholar 

  4. Th. Bäck, U. Hammel, and H.-P. Schwefel. Evolutionary computation: Comments on the history and current state. IEEE Transactions on Evolutionary Computation, 1:1-15, 1997.

    Google Scholar 

  5. J.C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, 1981.

    Book  MATH  Google Scholar 

  6. S. Blochwitz and J Eigermann. Creation and application of fuzzy rules to access the creditworthiness of enterprises. In Proceedings of the Fourth European Congress on Intelligent Techniques and Soft Computing (EUFIT `99), volume CD-ROM. Verlag Mainz, Aachen, 1999.

    Google Scholar 

  7. O. Cordon, A. Gonzalez, F. Herrera, and R. Perez. Encouraging cooperation in the genetic iterative rule learning approach for qualitative modeling. In Computing with Words in Information/Intelligent Systems, pages 95–117. Physica-Verlag, Heidelberg, 1999.

    Google Scholar 

  8. K. Honczarenko, A. Jardzioch, A. Piegat, and J Honczarenko. Application of soft computing method to Parkinson’s disease therapy. In Proceedings of the Fourth European Congress on Intelligent Techniques and Soft Computing (EUFIT `99), volume CD-ROM. Verlag Mainz, Aachen, 1999.

    Google Scholar 

  9. H. Jessen. Test-und Bewertungsverfahren zur regelbasierten Modellierung und Anwendung in der Lastprognose. Fortschritt-Berichte VDI, Reihe 8, Nr. 836. VDI Verlag, Düsseldorf, 2000.

    Google Scholar 

  10. H. Jessen. Test and rating strategies for automatic fuzzy rule generation and application to load prediction. In M. Mohammadian, editor, New Frontiers in Computational Intelligence and its Applications, pages 11–21. IOS Press, Amsterdam, 2000.

    Google Scholar 

  11. H. Jessen and T Slawinski. Mittelwertbasierter Regeltest und Bewertung für das Fuzzy-ROSA-Verfahren und Anwendung zur Lastprognose. In Tagungsband des 8. Workshops Fuzzy Control, number 0298, pages 67–81. VDI/VDE GMA-FA 5. 22, Faculty of Electrical Engineering and Information Technology, University of Dortmund, 1998.

    Google Scholar 

  12. H. Jessen and T. Slawinski. Test and rating strategies for data-based rule generation. Technical Report CI-39/98, Collaborative Research Center 531, University of Dortmund, 1998.

    Google Scholar 

  13. E. Juuso, D. Schauten, T. Slawinski, and H. Kiendl. Combination of linguistic equations and the fuzzy-ROSA method in dynamic simulation of a solar collector field. In Proceedings of TOOLMET2000 Symposium–Tool Environments and Development Methods for Intelligent Systems, pages 63–77. Oulun Yliopistopaino, Oulu, Finland, 2000.

    Google Scholar 

  14. H. Kiendl. Implizite Modellierung, inkrementeller Relevanzindex und Rauigkeitsmaß: neue Strategieelemente für die datenbasierte Modellierung. In Tagungsband des 10. Workshop Fuzzy Control des GMA-FA 5.22, pages 114. VDI/VDE GMA-FA 5.22, Research Reports Forschungszentrum Karlsruhe (FZKA 6509), Karlsruhe, 2000.

    Google Scholar 

  15. H. Kiendl. Verfahren zur Erzeugung von Stellgrößen am Ausgang eines Fuzzy-Reglers und Fuzzy-Regler hierfür. Technical report, 1994. Patent, DE 43 08 083.

    Google Scholar 

  16. H. Kiendl. System of controlling or monitoring processes or industrial plants employing a dual-line fuzzy unit. Technical report, 1998. Patent, U.S. 5,826, 251.

    Google Scholar 

  17. H. Kiendl. Decision analysis by advanced fuzzy systems. In J. Kacprzyk L. Zadeh, editor, Computing with Words in Information/Intelligent Systems, pages 223–242. Physica-Verlag, Heidelberg, 1999.

    Google Scholar 

  18. H. Kiendl and J. Albrecht. Verfahren zur sequentiellen Vorsteuerung eines Prozesses, 1997. Patent, DE 195 10 343.

    Google Scholar 

  19. H. Kiendl and W. Hansen. Quadratische Optimierung unter Nebenbedingungen und Anwendung in der Kraftwerksführung. Technical Report CI-119/02, Collaborative Research Center 531, University of Dortmund, 2002.

    Google Scholar 

  20. R. Klinkenberg and T. Slawinski. Wissensmanagement in der Computational Intelligence: Systematisierung der Beschreibung von Problemen, Methoden und Methodeneinsätzen. In Proceedings 11. Workshop Fuzzy Control des GMAFA 5.22, Dortmund, pages 78–99. VDI/VDE GMA-FA 5.22, Research Reports Forschungszentrum Karlsruhe (FZKA 6660), Karlsruhe, 2001.

    Google Scholar 

  21. P. Krause. Generierung von Takagi-Sugeno-Fuzzy-Systemen aus relevanten Fuzzy-Regeln. In Proceedings 10. Workshop Fuzzy Control des GMA-FA 5.22, Dortmund, pages 84–97. VDI/VDE GMA-FA 5.22, Research Reports Forschungszentrum Karlsruhe (FZKA 6509), Karlsruhe, 2000.

    Google Scholar 

  22. P. Krause. Datenbasierte Generierung von transparenten und genauen Fuzzy-Modellen für mehrdeutige Daten und komplexe Systeme. Fortschritt-Berichte VDI, Reihe 10, Nr. 691. VDI Verlag, Düsseldorf, 2001.

    Google Scholar 

  23. P. Krause, A. Krone, and T. Slawinski. Fuzzy System Identification by Generating and Evolutionary Optimizing Fuzzy Rule Bases Consisting of Relevant Fuzzy Rules. Technical Report CI-84/00, Collaborative Research Center 531, University of Dortmund, 2000.

    Google Scholar 

  24. P. Krause and T. Slawinski. Das Fuzzy-ROSA-Verfahren: Von der regelorientierten statistischen Analyse zur datenbasierten Generierung von interpretierbaren Takagi-Sugeno-Systemen. at-Automatisierungstechnik, 9 (49): 391–399, 2001.

    Article  Google Scholar 

  25. P Krause, D. Wiesmann, and T. Slawinski. Parallel evolutionary algorithms for optimizing data-based generated fuzzy systems. Technical Report CI-101/00, Collaborative Research Center 531, University of Dortmund, 2000.

    Google Scholar 

  26. T. Kretschmer, T. Born, C Gierend, and M. Born. Automatische Generierung von Fuzzy-Systemen in Verbrennungsprozessen. In Tagungsband 5tes Nationales DataAnalysis Symposium, pages 45–50. MIT GmbH, Aachen, 1999.

    Google Scholar 

  27. A. Krone. Advanced rule reduction concepts for optimizing efficiency of knowledge extraction. In Proceedings of the Fourth European Congress on Intelligent Techniques and Soft Computing (EUFIT `96), volume 2, pages 919–923. Verlag Mainz, Aachen, 1996.

    Google Scholar 

  28. A. Krone. Datenbasierte Generierung von relevanten Fuzzy-Regeln zur Modellierung von Prozesszusammenhängen und Bedienstrategien. Fortschritt-Berichte VDI, Reihe 10, Nr. 615. VDI Verlag, Düsseldorf, 1999.

    Google Scholar 

  29. A. Krone, T. Bäck, and P. Teuber. Evolutionäres Suchkonzept zum Aufstellen signifikanter Fuzzy-Regeln. at-Automatisierungstechnik, 44 (8): 405–411, 1996.

    Google Scholar 

  30. A. Krone, C. Frenck, and O. Russak. Design of a fuzzy controller for an alkoxylation process using the ROSA method for automatic rule generation. In Proceedings of the Third European Congress on Intelligent Techniques and Soft Computing (EUFIT ‘85), volume 2, pages 760–764. Verlag Mainz, Aachen, 1995.

    Google Scholar 

  31. A. Krone and H. Kiendl. Automatic generation of positive and negative rules for two-way fuzzy controllers. In Proceedings of the Second European Congress on Intelligent Techniques and Soft Computing (EUFIT ‘84), volume 1, pages 438–447. Verlag Mainz, Aachen, 1994.

    Google Scholar 

  32. A. Krone and H. Kiendl. WINROSA von Daten zu Regeln. MIT-Managment Intelligenter Technologien GmbH, Aachen, 1st edition, 1997. Manual.

    Google Scholar 

  33. A. Krone, P. Krause, and T. Slawinski. A new rule reduction method for finding interpretable and small rule bases in high dimensional search spaces. In Proceedings of the Ninth IEEE International Conference on Fuzzy Systems, (FUZZ-IEEE ‘00), San Antonio, TX, volume 2, pages 696–699. IEEE Press, Piscataway, NJ, 2000.

    Google Scholar 

  34. A. Krone, P. Krause, T. Slawinski, and R. Knicker. WINROSA 2.0 and DORA for Windows 6.2. In Symposium on System Identification (SYSID 2000), Santa Barbara, CA, pages 521–526. Elsevier Science, Amsterdam, 2000.

    Google Scholar 

  35. A. Krone and U. Schwane. Generating fuzzy rules from contradictory data of different control strategies and control performances. In Proceedings of the Fifth IEEE International Conference on Fuzzy Systems (FUZZ-IEEE ‘86), New Orleans, volume 1, pages 492–497. IEEE Press, Piscataway, NJ, 1996.

    Google Scholar 

  36. A. Krone and T. Slawinski. Data-based extraction of unidimensional fuzzy sets for fuzzy rule generation. In Proceedings of the Seventh IEEE International Conference on Fuzzy Systems (FUZZ-IEEE ‘88), Anchorage, AK, volume 2, pages 1032–1037. IEEE Press, Piscataway, NJ, 1998.

    Google Scholar 

  37. A. Krone and T. Slawinski. A distance measure for the mutation of fuzzy-rules. Technical Report CI-83/00, Collaborative Research Center 531, University of Dortmund, 2000.

    Google Scholar 

  38. A. Krone and H. Taeger. Relevance test for fuzzy rules. Technical Report CI-40/98, Collaborative Research Center 531, University of Dortmund, 1998.

    Google Scholar 

  39. A. Krone and H. Taeger. Data-based fuzzy rule test for fuzzy modelling. Fuzzy Sets and Systems, 126: 343–358, 2001.

    Article  MathSciNet  Google Scholar 

  40. K. Linke. Kraftwerksführungssystem der Verbundebene - Struktur, Entwicklungsstand, neue Konzepte. In FGE-Tagung 1996, pages 618–624. Energiewirtschaft und Technik Verlagsgesellschaft mbH, Essen, Energiewirtschaftliche Tagesfragen, Heft 10/1996, 1996.

    Google Scholar 

  41. W. Pedrycz. Fuzzy Control and Fuzzy Systems. Research Studies LTD, Taunton, UK, 1993.

    MATH  Google Scholar 

  42. B. M. Pfeiffer, J. Jäkel, A. Kroll, C. Kuhn, H. B. Kuntze, B. Lehmann, T. Slawinski, and V. Tews. Tutorial: Erfolgreiche Anwendungen Fuzzy Control. In Proceedings 11. Workshop Fuzzy Control des GMA-FA 5.22, Dortmund, pages 1–27. VDI/VDE GMA-FA 5.22, Research Reports Forschungszentrum Karlsruhe (FZKA 6660), Karlsruhe, 2001.

    Google Scholar 

  43. J. Praczyk. Entwurf von Fuzzy-Gütemaßen zur Prozeßbewertung. Fortschritt-Berichte VDI, Reihe 8, Nr. 796. VDI Verlag, Düsseldorf, 1999.

    Google Scholar 

  44. J. Praczyk, H. Kiendl, and T. Slawinski. Finding relevant process characteristics with a method for data-based complexity reduction. In B. Reusch, editor, Computational Intelligence: Theory and Applications (Proceedings of 6th Fuzzy-Days), pages 548–555. Springer, Berlin, 1999.

    Google Scholar 

  45. D. Schauten, B. Nicolaus, and H. Kiendl. Evolutionäres Verfahren zur Selektion relevanter Merkmalssätze für die datenbasierte Fuzzy-Modellierung. In Proceedings 11. Workshop Fuzzy Control des GMA-FA 5.22, Dortmund, pages 133147. VDI/VDE GMA-FA 5.22, Research Reports Forschungszentrum Karlsruhe (FZKA 6660), Karlsruhe, 2001.

    Google Scholar 

  46. D. Schauten, B. Nicolaus, and H. Kiendl. An evolutionary concept for selecting relevant sets of input variables for data-based fuzzy modeling. In Proceedings of the European Symposium on Intelligent Technologies, Hybrid Systems and their Implementation on Smart Adaptive Systems (E UNITE `01), volume CD-ROM. Verlag Mainz, Aachen, 2001.

    Google Scholar 

  47. D. Schauten, T. Slawinski, and H. Kiendl. Datenbasierte Generierung von situationsbezogenen Entscheidungsregeln für die Kraftwerksführung. Technical Report CI-110/01, Collaborative Research Center 531, University of Dortmund, 2001.

    Google Scholar 

  48. U. Schwane. Datenbasierte Generierung von Adaptionsregeln und Anwendung zur Erhöhung der Bahngenauigkeit eines Industrieroboters. Fortschritt-Berichte VDI, Reihe 8, Nr. 748. VDI Verlag, Düsseldorf, 1999.

    Google Scholar 

  49. T. Slawinski. Analyse und effiziente Generierung von relevanten Fuzzy Regeln in hochdimensionalen Suchräumen. Fortschritt-Berichte VDI, Reihe 10, Nr. 686. VDI Verlag, Düsseldorf, 2001.

    Google Scholar 

  50. T. Slawinski, H. Jessen, J. Praczyk, P. Krause, A. Krone, and H. Kiendl. Einsatz der datenbasierten Fuzzy-Modellierungen für komplexe Anwendungen. In Computational Intelligence im industriellen Einsatz, Tagung Baden-Baden, VDI-Berichte, Nr. 1526, pages 119–124. VDI/VDE GMA und GI, VDI-Verlag, Düsseldorf, 2000.

    Google Scholar 

  51. T. Slawinski, P. Krause, and H. Kiendl. Using individually tested rules for the data-based generation of interpretable rule bases with high accuracy. In J. Casillas, O. Cordon, F. Herrera, and L. Magdalena, editors, Trade-off Between Acuracy and Interpretability in Fuzzy Rule-Based Modeling, page (Accepted). Physica-Verlag, Heidelberg, 2002.

    Google Scholar 

  52. T. Slawinski, P. Krause, A. Krone, U. Hammel, and D. Wiesmann. Fuzzy Adaption der evolutionären Regelsuche im Fuzzy-ROSA-Verfahren. In Tagungsband des 9. Workshops Fuzzy Control des GMA-FA 5.22, Dortmund, number 0499, pages 14–39. VDI/VDE GMA-FA 5. 22, Faculty of Electrical Engineering and Information Technology, University of Dortmund, 1999.

    Google Scholar 

  53. T. Slawinski, A. Krone, U. Hammel, D. Wiesmann, and P. Krause. A hybrid evolutionary search concept for data-based generation of relevant fuzzy rules in high dimensional spaces. In Proceedings of IEEE International Conference on Fuzzy Systems (FUZZ-IEEE ‘89) Seoul, volume 3, pages 1432–1437. IEEE Press, Piscataway, NJ, 1999.

    Google Scholar 

  54. T. Slawinski, A. Krone, U. Hammel, D. Wiesmann, and M. Lindenblatt. A hybrid evolutionary search concept for data-based generation of relevant fuzzy rules in high dimensional spaces (extended version). Technical Report CI-50/98, Collaborative Research Center 531, University of Dortmund, 1998.

    Google Scholar 

  55. T. Slawinski, A. Krone, and H. Kiendl. Automatisierung durch datenbasierte Fuzzy-Modellierung von Prozessbedienern. In Computational Intelligence: neuronale Netze, evolutionäre Algorithmen, Fuzzy-Control im industriellen Einsatz, Tagung Berlin, 1998, VDI-Berichte, Nr. 1381, pages 203–219. VDI/VDE GMA, VDI-Verlag, Düsseldorf, 1998.

    Google Scholar 

  56. T. Slawinski, A. Krone, and P. Krause. Efficient design of a complete rule search in sparsely populated search spaces. Technical Report CI-77/99, Collaborative Research Center 531, University of Dortmund, 1999.

    Google Scholar 

  57. T. Slawinski, A. Krone, P. Krause, and H. Kiendl. The fuzzy-ROSA method: A statistically motivated fuzzy approach for data-based generation of small interpretable rule bases in high-dimensional search spaces. In M. Last, A. Kandel, and H. Bunke, editors, Data Mining and Computational Intelligence, pages 141–166. Physica-Verlag, Heidelberg, 2001.

    Google Scholar 

  58. T. Slawinski, J. Praczyk, U. Schwane, A. Krone, and H. Kiendl. Data-based generation of fuzzy rules for classification, prediction and control with the fuzzy-ROSA method. In European Control Congress (ECC ‘89), Karlsruhe, volume CD-ROM. Karlsruhe, 1999.

    Google Scholar 

  59. N. Thomaidis, G. Dounias, and Tselentis. Stock exchange market analysis using data engine and WINROSA. In Proceedings of the Fourth European Congress on Intelligent Techniques and Soft Computing (EUFIT `99), volume CD-ROM. Verlag Mainz, Aachen, 1999.

    Google Scholar 

  60. G. Vachkov. Fuzzy modeling examples by WINROSA. In Proceedings of the 2nd DataAnalysis Symposium. MIT GmbH, Aachen, 1998. Artikel Nr. 25299.

    Google Scholar 

  61. N. Xion. Designing Compact and Comprehensible Fuzzy Controllers Using Genetic Algorithms (Entwurf kompakter und interpretierbarer Fuzzy Controller mittels Genetischer Algorithmen). Berichte aus der Automatisierungstechnik. Shaker Verlag, Aachen, 2001.

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Information Technology, Control Engineering, University of Dortmund, 44221, Dortmund, Germany

    Harro Kiendl, Peter Krause, Daniel Schauten & Timo Slawinski

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  1. Harro Kiendl
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  2. Peter Krause
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  3. Daniel Schauten
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  4. Timo Slawinski
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Editors and Affiliations

  1. Fachbereich Informatik Lehrstuhl Informatik XI, Universität Dortmund, 44221, Dortmund, Germany

    Hans-Paul Schwefel

  2. Lehrstuhl Informatik II, Universität Dortmund, 44221, Dortmund, Germany

    Ingo Wegener

  3. Fakultät Maschinenbau Institut für Spannende Fertigung, Universität Dortmund, 44221, Dortmund, Germany

    Klaus Weinert

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Kiendl, H., Krause, P., Schauten, D., Slawinski, T. (2003). Data-Based Fuzzy Modeling for Complex Applications. In: Schwefel, HP., Wegener, I., Weinert, K. (eds) Advances in Computational Intelligence. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-05609-7_3

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  • DOI: https://doi.org/10.1007/978-3-662-05609-7_3

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