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Fusion of soft and hard computing: multi-dimensional categorization of computationally intelligent hybrid systems

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Abstract

The concept of fusion of soft computing and hard computing has rapidly gained importance over the last few years. Soft computing is known as a complementary set of techniques such as neural networks, fuzzy systems, or evolutionary computation which are able to deal with uncertainty, partial truth, and imprecision. Hard computing, i.e., the huge set of traditional techniques, is usually seen as the antipode of soft computing. Fusion of soft and hard computing techniques aims at exploiting the particular advantages of both realms. This article introduces a multi-dimensional categorization scheme for fusion techniques and applies it by analyzing several fusion techniques where the soft computing part is realized by a neural network. The categorization scheme facilitates the discussion of advantages or drawbacks of certain fusion approaches, thus supporting the development of novel fusion techniques and applications.

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References

  1. Zadeh LA (1997) What is soft computing? Soft Comput Fusion Found Methodol Appl 1(1):1

    Google Scholar 

  2. Mandl R, Sick B (2003) Messen, Steuern und Regeln mit ICONNECT—Visuelle, komponentenbasierte Entwicklung von Bild- und Signalverarbeitungsanwendungen. Friedrich Vieweg & Sohn Verlagsgesellschaft, Braunschweig

    Google Scholar 

  3. Dunham MH (2003) Data mining: introductory and advanced topics. Pearson Education, Upper Saddle River

    Google Scholar 

  4. Ovaska SJ (2004) Introduction to fusion of soft computing and hard computing. In: Ovaska SJ (ed) Computationally intelligent hybrid systems: the fusion of soft computing and hard computing, chapter 1. Wiley/IEEE Press, Hoboken, pp 5–30

  5. Ovaska SJ (ed) (2004) Computationally intelligent hybrid systems: the fusion of soft computing and hard computing. Wiley/IEEE Press, Hoboken

  6. Ovaska SJ, VanLandingham HF, Kamiya A (2002) Fusion of soft computing and hard computing in industrial applications: an overview. IEEE Trans Syst Man Cybern C Appl Rev 32(2):72–79

    Article  Google Scholar 

  7. Zhou C, Maravall D, Ruan D (2003) Autonomous robotic systems—soft computing and hard computing methodologies and applications. Studies in fuzziness and soft computing, vol 116. Physica-Verlag, Heidelberg

  8. Hayashi I, Umano M, Maeda T, Bastian A, Jain LC (1998) Acquisition of fuzzy knowledge by NN and GA—a survey of the fusion and union methods proposed in Japan. In: Second international conference on knowledge-based intelligent electronic systems, Adelaide, vol 1, pp 69–78

  9. Furuhashi T (2001) Fusion of fuzzy/neuro/evolutionary computing for knowledge acquisition. Proc IEEE 89(9):1266–1274

    Article  Google Scholar 

  10. Sick B (2002) On-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research. Mech Syst Signal Process 16(4):487–546

    Article  Google Scholar 

  11. Sick B, Grass W, Donner K (1999) Techniken der Sensorfusion bei der Verarbeitung mikrosystembasierter Signale. it+ti: Informationstechnik Technische Informatik 41(4):14–19

    Google Scholar 

  12. Brooks RR, Iyengar SS (1998) Multi-sensor fusion—fundamentals and applications with software. Prentice-Hall, Upper Saddle River

    Google Scholar 

  13. Klein LA (1993) Sensor and data fusion concepts and applications. Number TT14 in tutorial texts. SPIE Optical Engineering Press, Bellingham

  14. Sharkey A (ed) (1999) Combining artificial neural nets: ensemble and modular multi-net systems. Springer, Berlin Heidelberg New York

  15. Gruber C, Sick B (2003) Fast and efficient second-order training of the dynamic neural network paradigm. In: IEEE-INNS international joint conference on neural networks, Portland, vol 4, pp 2482–2487

  16. Bajramovic F, Gruber C, Sick B (2004) A comparison of first- and second-order training algorithms for dynamic neural networks. In: International joint conference on neural networks, Budapest, vol 2, pp 837–842

  17. Kohonen T (2001) Self-organizing maps, 3rd edn. Springer, Berlin Heidelberg New York

    MATH  Google Scholar 

  18. Haykin S (1999) Neural networks—a comprehensive foundation, 2nd edn. Prentice-Hall, Upper Saddle River

    MATH  Google Scholar 

  19. Poggio T, Girosi F (1989) A theory of networks for approximation and learning. A.I. Memo no. 1140, C.B.I.P. paper no. 31. Massachusetts Institute of Technology, Artificial Intelligence Laboratory & Center for Biological Information Processing, Whitaker College

  20. Waibel A, Hanazawa T, Hinton G, Shikano K, Lang KJ (1989) Phoneme recognition using time-delay neural networks. IEEE Trans Acoust Speech Signal Process 37(3):328–339

    Article  Google Scholar 

  21. Wan EA (1993) Finite impulse response neural networks with applications in time series prediction. PhD Thesis, Department of Electrical Engineering, Stanford University

  22. Gruber C, Sick B (2003) Processing short-term and long-term information with a combination of hard- and soft-computing techniques. In: IEEE international conference on systems, man, and cybernetics, Washington, DC, vol 1, pp 126–133

  23. Bach C, Bredl S, Kossa W, Sick B (2003) Calibration of self-organizing maps for classification tasks. In: IEEE international conference on systems, man, and cybernetics, Washington, DC, vol 3, pp 2877–2882

  24. Akhmetov DF, Dote Y, Ovaska SJ (2001) Fuzzy neural network with general parameter adaptation for modeling of nonlinear time-series. IEEE Trans Neural Netw 12(1):148–152 (Errata published in IEEE Trans Neural Netw 12(2):443)

    Google Scholar 

  25. Sick B (2002) Fusion of hard and soft computing techniques in indirect, online tool wear monitoring. IEEE Trans Syst Man Cybern C Appl Rev 32(2):80–91

    Article  Google Scholar 

  26. Sick B (2004) Indirect online tool wear monitoring. In: Ovaska SJ (ed) Computationally intelligent hybrid systems: the fusion of soft computing and hard computing, chapter 6. Wiley/IEEE Press, Hoboken, pp 169–198

  27. Sick B (2001) Tool wear estimation in turning with process-specific pre-processing and time-delay neural networks. Int J Smart Eng Syst Des 3(1):1–14

    Google Scholar 

  28. Gao XZ, Ovaska SJ (2001) Comparison of conventional and soft computing-based power control methods in mobile communications systems. Soft Comput Fusion Found Methodol Appl 5(4):287–296

    MATH  Google Scholar 

  29. Cheok AD (2004) Sensorless control of switched reluctance motors. In: Ovaska SJ (ed) Computationally intelligent hybrid systems: the fusion of soft computing and hard computing, chapter 4. Wiley/IEEE Press, Hoboken, pp 93 – 124

  30. Buchtala O, Hofmann A, Sick B (2003) Fast and efficient training of rbf networks. In: Kaynak O, Alpaydin E, Oja E, Xu L (eds) Artificial neural networks and neural information processing—ICANN/ICONIP 2003 (joint international conference ICANN/ICONIP 2003), Istanbul, number 2714 in LNCS. Springer, Berlin Heidelberg New York, pp 43–51

  31. Buchtala O, Neumann P, Sick B (2003) A strategy for an efficient training of radial basis function networks for classification applications. In: Proceedings of the IEEE-INNS international joint conference on neural networks, Portland, vol 2, pp 1025–1030

  32. Buckner GD (2004) Estimation of uncertainty bounds for linear and nonlinear robust control. In: Ovaska SJ (ed) Computationally intelligent hybrid systems: the fusion of soft computing and hard computing, chapter 5. Wiley/IEEE Press, Hoboken, pp 129–164

  33. Fuchs E, Donner K (1997) Fast least-squares polynomial approximation in moving time windows. In: International conference on acoustics, speech and signal processing, Munich, vol 3, pp 1965–1968

  34. Ulbricht C, Dorffner G, Canu S, Guillemyn D, Marijuán G, Olarte J, Rodríguez C, Martín I (1992) Mechanisms for handling sequences with neural networks. In: Dagli CH, Burke LI, Shin YC (eds) Intelligent engineering systems through artificial neural networks, vol 2. ASME Press, New York, pp 273–278

  35. Fuchs E, Sick B (1999) Using temporal information in input features of neural networks. In: Ninth international conference on artificial neural networks incorporating the IEE conference on artificial neural networks, Edinburgh, vol 1, pp 467–472 (IEE conference publication no. 470)

  36. Bredl S (2004) Implementierung der Grundfunktionalität von SOM und von nichthierarchischen Clusteringalgorithmen in der Simulationsumgebung NNSim. Technical report, Universität Passau, Fakultät für Mathematik und Informatik (final report of a programming project)

  37. Stein M (1999) Interpolation of spatial data. Some theory for kriging. Springer, Berlin Heidelberg New York

    MATH  Google Scholar 

  38. Ashimov AA, Syzdykov DJ (1981) Identification of high dimensional system by the general parameter method. In: Eighth IFAC world congress, Kyoto, vol 6, pp 32–37

  39. Dote Y, Ovaska SJ, Gao XZ (2000) Fault detection of automobile transmission gears using general parameter methods. J Robot Mechatron 13(6):702–705

    Google Scholar 

  40. Sterritt R (2003) Autonomic computing: the natural fusion of soft computing and hard computing. In: International conference on systems, man and cybernetics, Washington, DC, vol 5, pp 4754–4759

  41. Weiser M (1993) Some computer science issues in ubiquitous computing. Commun ACM 36(7):75–84

    Article  Google Scholar 

  42. Satyanarayanan M (2001) Pervasive computing: vision and challenges. IEEE Pers Commun 8(4):10–17

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, University of Passau, 94030, Passau, Germany

    Bernhard Sick

  2. Institute of Intelligent Power Electronics, Helsinki University of Technology, 02150, Espoo, Finland

    Seppo J. Ovaska

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  1. Bernhard Sick
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  2. Seppo J. Ovaska
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Correspondence to Bernhard Sick.

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Sick, B., Ovaska, S.J. Fusion of soft and hard computing: multi-dimensional categorization of computationally intelligent hybrid systems. Neural Comput & Applic 16, 125–137 (2007). https://doi.org/10.1007/s00521-006-0045-y

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  • DOI: https://doi.org/10.1007/s00521-006-0045-y

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