Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-25T15:53:25.155Z Has data issue: false hasContentIssue false
  • English
  • Français

A fuzzy logic system for expert systems

Published online by Cambridge University Press:  27 February 2009

Te-Chuan Chang ,
C. William Ibbs  and
Keith C. Crandall
Te-Chuan Chang
Affiliation:
Department of Civil Engineering, University of California, Berkeley, CA 94720, U.S.A.
C. William Ibbs
Affiliation:
Department of Civil Engineering, University of California, Berkeley, CA 94720, U.S.A.
Keith C. Crandall
Affiliation:
Department of Civil Engineering, University of California, Berkeley, CA 94720, U.S.A.
Get access Rights & Permissions [Opens in a new window]

Abstract

Using the theory of fuzzy sets, this paper develops a fuzzy logic reasoning system as an augmentation to a rule-based expert system to deal with fuzzy information. First, fuzzy set theorems and fuzzy logic principles are briefly reviewed and organized to form a basis for the proposed fuzzy logic system. These theorems and principles are then extended for reasoning based on knowledge base with fuzzy production rules. When an expert system is augmented with the fuzzy logic system, the inference capability of the expert system is greatly expanded; and the establishment of a rule-based knowledge base becomes much easier and more economical. Interpretations of the system’s power and possible future research directions conclude the paper.

Type
Research Article
Information
AI EDAM , Volume 2 , Issue 3 , August 1988 , pp. 183 - 193
Copyright
Copyright © Cambridge University Press 1988

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Chang, T. C. 1987. Network resource allocation using an expert system with fuzzy logic reasoning. Construction Engineering and Management Division, Department of Civil Engineering, University of California, Berkeley, Technical Report No. 11.Google Scholar
De La Garza, J. M. 1988. A knowledge engineering approach to the analysis and evaluation of vertical construction schedules. PhD Thesis, University of Illinois.Google Scholar
Driankov, D. 1987. Inference with a single fuzzy conditional proposition. Fuzzy Sets and Systems 24, 51–63.CrossRefGoogle Scholar
Ernst, C. J. 1982. An approach to management expert systems using fuzzy logic. In Yager, R. R. (ed.), Fuzzy Set and Possibility Theory. New York: Pergamon Press, pp. 196–203.Google Scholar
Fukami, S., Mizumoto, M. and Tanaka, K. 1980. Some considerations on fuzzy conditional inference. Fuzzy Sets and Systems 4, 243–273.CrossRefGoogle Scholar
Mizumoto, M. 1982 a. Fuzzy inference using max-drastic composition in the compositional rule of inference. In Gupta, M. M. and Sanchez, E. (eds), Approximate Reasoning in Decision Analysis. New York: North-Holland, pp. 67–76.Google Scholar
Mizumoto, M. 1982 b. Fuzzy reasoning with a fuzzy conditional proposition ‘IF … THEN … ELSE …” In Yager, R. R. (ed.), Fuzzy Set and Possibility Theory. New York: Pergamon Press, pp. 211–223.Google Scholar
Mizumoto, M. and Zimmermann, H.-J. 1982. Comparison of fuzzy reasoning methods. Fuzzy Sets and Systems 8, 253–283.CrossRefGoogle Scholar
Negoita, C. V. 1985. Expert Systems and Fuzzy Systems. New York: Benjamin/Cummings.Google Scholar
Zadeh, L. A. 1965. Fuzzy sets. Information and Control 8, 338–353.CrossRefGoogle Scholar
Zadeh, L. A. 1973. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions of Systems, Man, and Cybernetics SMC-3, 28–44.CrossRefGoogle Scholar
Zadeh, L. A. 1975 a. Calculus of fuzzy restrictions. In Zadeh, L. A., Fu, K. S., Tanaka, K. and Shimura, M. (eds), Fuzzy Sets and Their Application to Cognitive and Decision Processes. New York: Academic Press, pp. 1–39.Google Scholar
Zadeh, L. A. 1975b. Fuzzy logic and approximate reasoning. Synthese 30, 407–428.CrossRefGoogle Scholar
Zadeh, L. A. 1983. The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets and Systems 11, 199–227.CrossRefGoogle Scholar