Estimation of Isoseismal Area

  • Chongfu Huang
  • Yong Shi
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 99)


In this chapter1, based on the normal diffusion and the feedforward neural network with backpropagation algorithm (BP), we suggest a hybrid fuzzy neural network to estimate isoseismal area by earthquake magnitude. In section 9.1 we give the outline of estimation of isoseismal area. In section 9.2, we give a brief review of current methods for the construction of fuzzy relationships. Section 9.3 suggests the information diffusion function to produce if-then rules from observations. In section 9.4, we propose a model for pattern smoothing to assist a BP neural network to acquire knowledge from the data. In section 9.5, we give the architecture of the hybrid model which consists of an information-diffusion approximate reasoning and a conventional BP neural network. In section 9.6, we use the model to estimate isoseismal area by earthquake magnitude. The chapter is then summarized with a conclusion in section 9.7.


Fuzzy Rule Earthquake Magnitude Fuzzy Subset Fuzzy Relation 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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  1. 1.
    Bollinger, G.A., Chapman, M.C., and Sibol, M.S. (1993), A comparison of earthquake damage areas as a function of magnitude across the United States. Bulletin of the Seismological Society of America Vol. 83, pp. 1064–80Google Scholar
  2. 2.
    Cavallini, F. and Rebez, A. (1996), Representing earthquake intensity magnitude relationship with a nonlinear function. Bulletin of the Seismological Society of America, Vol. 86, pp. 73–78Google Scholar
  3. 3.
    Dickerson, J.A. and Kosko, B. (1993), Fuzzy function learning with covariance ellipsoids. Proceedings of FUZZ-IEEE’93, California, pp. 1162–1167Google Scholar
  4. 4.
    Dickerson, J.A. and Kosko, B. (1993), Fuzzy function approximation with supervised ellipsoidal learning. World Congress on Neural Networks ( WCNN ), Vol.II, pp. 9–13.Google Scholar
  5. 5.
    Fukushima, Y., Gariel, J.C. and Tanaka, R. (1995), Site-dependent attenuation relations of seismic motion parameters depth using borehole data, Bulletin of the Seismological Society of America, Vol. 85, pp. 1790–1804Google Scholar
  6. 6.
    Gupta, I.N. and Nuttli3O.W.(1976), Spatial attenuation of intensities fro central U.S. earthquake. Bulletin Seismological Society of America, Vol. 66, pp. 743–751Google Scholar
  7. 7.
    Hernandez, J.V., Moore, K. and Elphic, R.(1995), Sensor fusion and nonlinear prediction for anomalous event detection. Proceedings of the SPIE–The International Society for Optical Engineering, Vol. 2484, pp. 102–112Google Scholar
  8. 8.
    Hodgson,J.H. (1964), Earthquakes and Earth Structure. Prentice-Hell, Inc., Englewood Cliffs, New JerseyGoogle Scholar
  9. 9.
    Howell, B.F. and Schultz, T.R. (1975), Attenuation of Modified Mercalli intensity with distance from the epicenter. Bulletin Seismological Society of America, Vol. 65, pp. 651–665Google Scholar
  10. 10.
    Huang, C.F. (1995), Fuzziness of incompleteness and information diffusion principle. Proceedings of FUZZ-IEEE/IFES’95, Yokoham, Japan, pp. 1605–1612Google Scholar
  11. 11.
    Huang, C.F. (1997), Principle of information diffusion. Fuzzy Sets and Systems, Vol. 91, No. 1, pp. 69–90MathSciNetMATHCrossRefGoogle Scholar
  12. 12.
    Huang, C.F. and Jiading, W. (1995), Technology of Fuzzy Information Optimization Processing and Applications, Beijing University of Aeronautics and Astronautics Press, Beijing. (in Chinese)Google Scholar
  13. 13.
    Huang, C.F. and Leung, Y. (1999), Estimating the relationship between isoseismal area and earthquake magnitude by hybrid fuzzy-neural-network method, Fuzzy Sets and Systems, Vol. 107, No. 2, pp. 131–146MATHCrossRefGoogle Scholar
  14. 14.
    Huang, C.F. and Liu, Z.R. (1985), Isoseismal area estimation of Yunnan Province by fuzzy mathematical method. Fen Deyi and Liu Xihui (eds): Fuzzy Mathematics in Earthquake Researches. Seismological Press, Beijing, pp. 185195Google Scholar
  15. 15.
    Kim, H.M. and Kosko, B. (1996), Fuzzy prediction and filtering in impulsive noise. Fuzzy Sets and Systems, Vol. 77, pp. 15–33CrossRefGoogle Scholar
  16. 16.
    Kosko, B. (1992), Neural Networks and Fuzzy Systems, Prentice Hall, Englewood Cliffs, New JerseyGoogle Scholar
  17. 17.
    Lee, H.M. and Lu, B.H. (1994), FUZZY BP: a neural network model with fuzzy inference. IEEE Internat. Conf. on Neural Networks, Orlando, Florida, pp. 15831588Google Scholar
  18. 18.
    Lee, H.M., Lu, B.H. and Lin, F.T. (1995), A fuzzy neural network model for revising imperfect fuzzy rules. Fuzzy Sets and Systems, Vol., pp. 25–45Google Scholar
  19. 19.
    Liu, Z.R. and Huang, C.F. et al (1987), A fuzzy quantitative study on the effect of active fault distribution on isoseismal area in Yunnan, Journal of seismology, No. 1, pp. 9–16. (in Chinese)Google Scholar
  20. 20.
    Lomnitz, C. and Rosenblueth, E. (1976), Seismic Risk and Engineering Decisions. Elsevier Scientific Publishing Company, AmsterdamGoogle Scholar
  21. 21.
    Mamdani, E.H. (1977), Application of fuzzy logic to Approximate reasoning using linguistic synthesis. IEEE Transactions on Computer, Vol.26, pp. 11821191Google Scholar
  22. 22.
    Monostori, L. and Egresits, C. (1994), Modelling and monitoring of milling through neuro-fuzzy techniques. Proceedings of Intelligent Manufacturing Systems (IMS’94), Vienna, Austria, pp. 463–468Google Scholar
  23. 23.
    Pao, Y.H. (1989), Adaptive Pattern Recognition and Neural Networks, Addison-Wesley, Reading, MAMATHGoogle Scholar
  24. 24.
    Rhee, F.C. -H. and Krishnapuram, R. (1993), Fuzzy rule generation methods for high-level computer vision. Fuzzy Sets and Systems, Vol. 60, pp. 245–258CrossRefGoogle Scholar
  25. 25.
    Rubeis-V, V., Gasparini, C., Maramai, A., Murru, M. and Tertulliani, A. (1992), The uncertainty and ambiguity of isoseismal maps. Earthquake Engineering & Structural Dynamics, Vol.21, Iss: 6, pp.509-Google Scholar
  26. 26.
    Rumelhart, D.E. and McClelland, J.L. (1973), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vols. 1 and 2, MIT, Press, Cambridge, MAGoogle Scholar
  27. 27.
    Stephens, J.E. and VanLuchene, R.D. (1994), Integrated assessment of seismic damage in structures. Microcomputers in Civil Engineering, Vol.9, Iss: 2, pp. 119–128Google Scholar
  28. 28.
    Togai, M. and Watanabe, H. (1986), Expert system on a chip: an engine for real-time approximate reasoning. IEEE Expert, Vol. 1, No. 3, pp. 55–62CrossRefGoogle Scholar
  29. 29.
    Zadeh, L.A. (1974), On the analysis of large scale system. Gottinger, H. (ed.): Systems Approaches and Environment Problems, Vandenhoeck and Ruprecht, Gottingen, pp. 23–37Google Scholar
  30. 30.
    Zadeh, L.A. (1983), Acomputational approach to fuzzy quantifiers in natural languages. Computers and mathematics, Vol. 9, pp. 149–184MathSciNetMATHGoogle Scholar
  31. 31.
    Zadeh, L.A. (1995), Fuzzy control, fuzzy graphs, and fuzzy inference. in: Yam, Y. and Leung, K.S. (eds): Future Directions of Fuzzy Theory and Systems, World Scientific, Singapore, pp. 1–9.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Chongfu Huang
    • 1
  • Yong Shi
    • 2
  1. 1.Institute of Resources ScienceBeijing Normal UniversityBeijingChina
  2. 2.College of Information Science and TechnologyUniversity of Nebraska at OmahaOmahaUSA

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