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Theory and Practice on Information Granule Matrix

  • Ye Xue
  • Chongfu Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Information Gain Earthquake Magnitude Information Granule Trained Neural Network Fuzzy Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 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)CrossRefGoogle Scholar
  2. 2.
    Kosko, B.: Fuzzy Engineering. Prentice-Hall, Upper Saddle River (1997)MATHGoogle Scholar
  3. 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. 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. 5.
    Huang, C.F., Moraga, C.: A Diffusion-neural-network for Learning From Small Samples. International Journal of Approximate Reasoning 35, 137–161 (2004)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Huang, C., Shi, Y.: Towards Efficient Fuzzy Information Processing — Using the Principle of Information Diffusion. Physica-Verlag (Springer), Heidelberg (2002)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ye Xue
    • 1
    • 2
  • Chongfu Huang
    • 1
  1. 1.Institute of Disaster and Public Security, College of Resources ScienceBeijing Normal UniversityBeijingChina
  2. 2.Department of MathematicsTaiyuan University of TechnologyTaiyuan, ShanxiChina

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