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Estimation of Isoseismal Area

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

Abstract

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.

Keywords

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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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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