Fuzzy Control pp 265-273 | Cite as

Natural Language Input for Fuzzy Diagnosis

  • Norihide Sano
  • Ryoichi Takahashi
Part of the Advances in Soft Computing book series (AINSC, volume 6)


Several kinds of diagnosis techniques based on the kinetic or statistical model of a nuclear plant have been already utilized in operation. Rule-based approaches have been proposed as well to realize a flexible diagnostic method with use of fuzzy sets, which pay attention at the rules describing the relationship between the cause and symptom of the failures. These might be able to infer the failure by modus tollens using implications to represent the relation between the cause and symptom. In the first step of a diagnosis, it needs a broad outline of the failure from slightly symptoms. The present paper attempts to express the natural language input for fuzzy diagnosis with a dialog box on Visual C++.


Flow Rate Increase Fuzzy Relation Inverse Process Steam Line Modus Tollens 
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.
    Tsukamoto, Y.: Fuzzy Logic Based on Lukasiewicz Logic and its Implications to Diagnosis and Control. Dissertation, Tokyo Institute of Technology (1977)Google Scholar
  2. 2.
    Maruyama, Y., Takahashi, R.: Application of Fuzzy Reasoning to Failure Diagnosis. J. Atomic Energy Soc. Japan 27 (1985) 851–860 (in Japanese)CrossRefGoogle Scholar
  3. 3.
    Takahashi, R., Maruyama, Y.: Practical Expression of Exception to Diagnosis. Bull. Res. Lab. Nucl. Reactors Tokyo Inst. Technol. 12 (1987) 50–53Google Scholar
  4. 4.
    Suguri, S.: Reactor Safety Engineering. Nikkan Kogyo Shinbunsha, Tokyo (1975) (in Japanese)Google Scholar
  5. 5.
    Maruyama, Y., Takahashi, R.: Practical Expression for Exception and its Utilization to Leakage Identification of Power Plant. Fuzzy Sets and Systems 34 (1990) 1–13MathSciNetCrossRefGoogle Scholar
  6. 6.
    Sano, N., Takahashi, R.: Solutions on Fuzzy Diagnosis by Inverse Process. Proc. 6th Zittau Fuzzy-Colloquium, Zittau, Germany (1998) 7–12Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Norihide Sano
    • 1
  • Ryoichi Takahashi
    • 1
  1. 1.Dept. of Communications and InformaticsShizuoka Sangyo UniversityFujieda, ShizuokaJapan

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