Skip to main content
SpringerLink
Log in

Bio-inspired Self-Organizing Relationship Network as Knowledge Acquisition Tool and Fuzzy Inference Engine

  • Chapter
Computational Intelligence: Research Frontiers (WCCI 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5050))

Included in the following conference series:

Abstract

Since the SOM visualizes the similarity of raw information on the competitive layer, it can be utilized in the field of pattern classification, data analysis, and so on. However, it cannot model the input-output characteristics of the system of interest. In order to squeeze out the input-output relationship from the data set with evaluation obtained by trial and error, the novel modeling tool was developed by the author (1999), which is the extension of SOM and in which the input-output relationship of the system is mapped onto the competitive layer. The system is named as self-organizing relationship network (SOR network). A set of units on the competitive layer of the SOR network after learning exhibits a set of typical input-output characteristics of the system of interest and thus the network achieves the knowledge acquisition (IF-THEN rules) from the raw data with evaluation and the effective fuzzy inference with defuzzification. The plenary talk presents the tutorial aspects of the SOR network and an application to an intelligent control.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Penfield, W., Rasmussen, T.: The Cerebral Cortex of Man. Macmillan, New York (1952)

    Google Scholar 

  2. Kohonen, T.: Self-organizing formation of topologically correct feature maps. Biological Cybernetics 43(1), 59–69 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  3. Kohonen, T.: Analysis of a simple self-organizing process. Biological Cybernetics 44(2), 135–140 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  4. Yamakwa, T., Horio, K.: Self-organizing relationship (SOR) network. IEICE Transactions on Fundamentals E82-A, 1674–1678 (1999)

    Google Scholar 

  5. Horio, K., Haraguchi, T., Yamakawa, T.: An Intuitive Contrast Enhancement of an Image Data Employing the Self-Organizing Relationship (SOR) Network. In: Proceedings of International Joint Conference on Neural Networks (IJCNN 1999), pp. 10–16 (1999)

    Google Scholar 

  6. Horio, K., Yamakawa, T.: Adaptive Self-Organizing Relationship Network and Its Application to Adaptive Control. In: Proceedings of the 6th International Conference on Softcomputing and Information/Intelligent Systems (IIZUKA 2000), pp. 299–304 (2000)

    Google Scholar 

  7. Laumond, J.P., Sekhavat, S., Lamiraux, F.: Guidelines in Nonholonomic Motion Planning for Mobile Robots. Robot Motion Planning and Control, 229 (1998)

    Google Scholar 

  8. Brockett, R.W.: Asymptotic stability and feedback stabilization. In: Brockket, R., Millman, R., Sussman, H. (eds.) Differential Geometric Control Theory, pp. 181–191. Birkhauser, Boston (1983)

    Google Scholar 

  9. Pomet, J.B.: Explicit design of time-varying stabilization control laws for a class of controllable systems without drift. Systems & Control Letters 18(2), 147–158 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  10. Tilbury, D., Murray, R.M., Sastry, S.S.: Trajectory Generation for the N-Trailer Problem Using Goursat Normal Form. IEEE Transactions on Automatic Control 40(5), 802–819 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  11. Sampei, M., Tamura, T., Kobayashi, T., Shibui, N.: Arbitrary path tracking control of articulated vehicles using nonlinear control theory. IEEE Transactions on Control Systems Technology 1(4), 587–592 (1995)

    Google Scholar 

  12. Kolmanovsky, M., Reyhanoglu, M., McClamroch, N.H.: Switched mode feedback control laws for nonholonomic systems in extended power form. Systems & Control Letters 27(1), 29–36 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  13. Prieur, C., Astolfi, A.: Robust stabilization of chained systems via hybrid control. In: Proceedings of the 41st IEEE Conference on Decision and Control, pp. 522–527 (2002)

    Google Scholar 

  14. Nguyen, D., Widrow, B.: The truck backer-upper: an example of self-learning in neural network. In: Proceedings of International Joint Conference on Neural Networks (IJCNN 1989), pp. 357–363 (1989)

    Google Scholar 

  15. Kong, S.G., Kosko, B.: Adaptive fuzzy-systems for backing-up a truck-and-trailer. IEEE Transactions on Neural Networks 3(2), 211–223 (1992)

    Article  Google Scholar 

  16. Tanaka, K., Sano, M.: A robust stabilization problem of fuzzy control systems and its application to backing up control of a truck-trailer. IEEE Transactions on Fuzzy Systems 2(2), 119–133 (1994)

    Article  Google Scholar 

  17. Ichihashi, H., Miyoshi, T., Nagasaka, K., Tokunaga, M., Wakamatsu, T.: A Neurofuzzy Approach to Variational Problems by Using Gaussian Membership Functions. International Journal of Approximate Reasoning 13(4), 287–302 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  18. Hong, C.S., Won, J.M., Lee, J.S.: Multi-Thread Evolutionary Programming and Its Application to Truck-and-Trailer Backer-Upper Control. IEICE Transactions on Fundamentals E84-A(2), 597–603 (2001)

    Google Scholar 

  19. Zadeh, L.A.: Fuzzy Sets. Information and Control 8, 338–353 (1965)

    Article  MATH  MathSciNet  Google Scholar 

  20. Zadeh, L.A.: The concept of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man and Cybernetics 3(1), 28–44 (1973)

    MATH  MathSciNet  Google Scholar 

  21. Zadeh, L.A.: Knowledge representation in fuzzy logic. IEEE Transactions on Knowledge and Data Engineering 1, 89–100 (1989)

    Article  Google Scholar 

  22. Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7(1), 1–13 (1975)

    Article  MATH  Google Scholar 

  23. Mamdani, E.H.: Applications of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Transactions on Computers 26(12), 1182–1191 (1977)

    Article  MATH  Google Scholar 

  24. Takagi, T., Sugeno, M.: Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Transactions on Systems, Man and Cybernetics 15(1), 116–132 (1985)

    MATH  Google Scholar 

  25. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)

    Google Scholar 

  26. Koga, T., Horio, K., Yamakawa, T.: Applications of brain-inspired SOR network to controller design and knowledge acquisition. In: Proceedings of 5th Workshop on Self-Organizing Maps (WSOM 2005), CD-ROM (2005)

    Google Scholar 

  27. Koga, T., Horio, K., Yamakawa, T.: The Self-Organizing Relationship (SOR) Network Employing Fuzzy Inference Based Heuristic Evaluation. Neural Networks 19, 799–811 (2006)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Brain Science and Engineering, Faculty of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu, Kitakyushu, 808-0196, Japan

    Takeshi Yamakawa & Takanori Koga

  2. Fuzzy Logic Systems Institute (FLSI), 680-41 Kawazu, Iizuka, Fukuoka, 820-0067, Japan

    Takeshi Yamakawa

Authors
  1. Takeshi Yamakawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Takanori Koga
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Jacek M. Zurada Gary G. Yen Jun Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Yamakawa, T., Koga, T. (2008). Bio-inspired Self-Organizing Relationship Network as Knowledge Acquisition Tool and Fuzzy Inference Engine. In: Zurada, J.M., Yen, G.G., Wang, J. (eds) Computational Intelligence: Research Frontiers. WCCI 2008. Lecture Notes in Computer Science, vol 5050. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68860-0_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-68860-0_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68858-7

  • Online ISBN: 978-3-540-68860-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation