Towards Efficient Fuzzy Information Processing

Using the Principle of Information Diffusion

  • Chongfu Huang
  • Yong Shi

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 99)

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Principle of Information Diffusion

    1. Front Matter
      Pages 1-1
    2. Chongfu Huang, Yong Shi
      Pages 3-21
    3. Chongfu Huang, Yong Shi
      Pages 23-54
    4. Chongfu Huang, Yong Shi
      Pages 55-80
    5. Chongfu Huang, Yong Shi
      Pages 81-127
    6. Chongfu Huang, Yong Shi
      Pages 129-168
    7. Chongfu Huang, Yong Shi
      Pages 169-189
    8. Chongfu Huang, Yong Shi
      Pages 191-212
  3. Applications

    1. Front Matter
      Pages 213-213
    2. Chongfu Huang, Yong Shi
      Pages 215-246
    3. Chongfu Huang, Yong Shi
      Pages 247-271
    4. Chongfu Huang, Yong Shi
      Pages 273-295
    5. Chongfu Huang, Yong Shi
      Pages 297-324
    6. Chongfu Huang, Yong Shi
      Pages 325-363
  4. Back Matter
    Pages 365-370

About this book


When we learn from books or daily experience, we make associations and draw inferences on the basis of information that is insufficient for under­ standing. One example of insufficient information may be a small sample derived from observing experiments. With this perspective, the need for de­ veloping a better understanding of the behavior of a small sample presents a problem that is far beyond purely academic importance. During the past 15 years considerable progress has been achieved in the study of this issue in China. One distinguished result is the principle of in­ formation diffusion. According to this principle, it is possible to partly fill gaps caused by incomplete information by changing crisp observations into fuzzy sets so that one can improve the recognition of relationships between input and output. The principle of information diffusion has been proven suc­ cessful for the estimation of a probability density function. Many successful applications reflect the advantages of this new approach. It also supports an argument that fuzzy set theory can be used not only in "soft" science where some subjective adjustment is necessary, but also in "hard" science where all data are recorded.


Analysis Racter cognition fuzzy fuzzy set kernel model

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

Bibliographic information

  • DOI
  • Copyright Information Physica-Verlag Heidelberg 2002
  • Publisher Name Physica, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-7908-2511-4
  • Online ISBN 978-3-7908-1785-0
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site
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