Skip to main content

Theory and Practice on Information Granule Matrix

  • Conference paper
  • 1597 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4223))

Abstract

In this paper, a new framework called information granule matrix is suggested to illustrate a given granule sample for showing its information structure. The new framework does not any extra condition but the observations. An information granule matrix can be turned into a fuzzy relation matrix for fuzzy inference. The concept of information granule matrix is firstly formulated. Then information granule matrix is shown by a simple example and discussed from the meaning of mechanism. To display the advantage of the new framework, it is compared with some existed methods. We also use our suggested framework to illustrate the relationship between earthquake magnitude M and isoseismal area S. The result shows that the new model is better than both Linear Regression and BP Network.

Project Supported by National Natural Science Foundation of China, No. 40371002.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Gori, M., Tesi, A.: On the Problem of Local Minima in Back-propagation. IEEE Transactions on Pattern Analysis and Machine Intelligence 14, 76–86 (1992)

    Article  Google Scholar 

  2. Kosko, B.: Fuzzy Engineering. Prentice-Hall, Upper Saddle River (1997)

    MATH  Google Scholar 

  3. Ruan, D., Huang, C.: Fuzzy Sets and Fuzzy Information Granulation Theory — Key Selected by Zadeh, L.A. Beijing Normal University Press, Beijing (2000)

    Google Scholar 

  4. Huang, C., Xue, Y.: Some Concepts and Methods of Information Granule Diffusion. In: Hu, X., Liu, Q., Skowron, A., Lin, T.Y., Yager, R.R., Zhang, B. (eds.) Proceedings of the 2005 IEEE International Conference on Granular Computing, vol. I, pp. 28–33 (2005)

    Google Scholar 

  5. Huang, C.F., Moraga, C.: A Diffusion-neural-network for Learning From Small Samples. International Journal of Approximate Reasoning 35, 137–161 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  6. Huang, C., Shi, Y.: Towards Efficient Fuzzy Information Processing — Using the Principle of Information Diffusion. Physica-Verlag (Springer), Heidelberg (2002)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Xue, Y., Huang, C. (2006). Theory and Practice on Information Granule Matrix. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_36

Download citation

  • DOI: https://doi.org/10.1007/11881599_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45916-3

  • Online ISBN: 978-3-540-45917-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics