Advertisement

System Identification for Rule-Based Systems

  • Ian S. Shaw
Chapter
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 457)

Abstract

Rule-based fuzzy systems translate qualitative, vague and imprecisely formulated human experience and judgment into control rules. The first section presents the technique to capture this experience. As has been said in Chapter 2, it is the human operator that is being identified while he is controlling the plant/process, thus the rules of the fuzzy control algorithm will constitute an inverse of the plant/process input-output relationship. Membership functions are determined empirically, on the basis of given guidelines, by trial-and-error. In another case, the control system designer himself is called upon to act as a human operator and formulate the rules on the basis of his engineering judgment rather than previous experience with the system. He still uses fuzzy rules and membership functions and the inverse aspects of human control still prevail. As before, membership functions are determined empirically, on the basis of given guidelines, or by trial-and-error. A further variety of the previous methods occur when the variables are inherently fuzzy, i.e. cannot be quantified in any way. This aspect affects mostly the choice of membership functions which, in this case, carry the bulk of the empirical knowledge of the human operator or designer, while the rules are established as before. In addition, a brief overview of methods that avoid the use of human operator interviews and “automatically” generate rules and/or membership functions are presented for the case when no experienced human operators are available. In this case, the rules and/or membership functions are learned from on-line measurements.

Keywords

Membership Function Fuzzy Rule Fuzzy Model Fuzzy Control Fuzzy Controller 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. CHILVERS RAH: “Fuzzy Control of High Power Arc Furnace”. June 1996. M. Sc.Thesis, Rand Afrikaans University, Johannesburg, Republic of South Africa.Google Scholar
  2. HARRIS CJ, MOORE CG, BROWN M: “Intelligent Control: Aspects of Fuzzy Logic and Neural Nets”. World Scientific Publ, 1993, Singapore, ISBN 981-02-1042-6.zbMATHGoogle Scholar
  3. KOSKO B: Fuzzy Thinking. Hyperion, New York, 1933.Google Scholar
  4. PROCZYK TJ, MAMDANIEH: “A Linguistic Self-Organizing Process Controller.” Automatica, 1979, Vol 15, pp 15–30.CrossRefGoogle Scholar
  5. SUGENO M, KANG GT: “Structure Identification of Fuzzy Model.” Fuzzy Sets and Systems, 1988, Vol 28, Nol, ppl5–33.MathSciNetGoogle Scholar
  6. TAKAGI T, SUGENO M:“Fuzzy Identification of Systems and Its Applications to Modeling and Control.”IEEE Trans.Sys., Man, Cybern., 1985;SMC-15;1;116–132.,CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Ian S. Shaw
    • 1
  1. 1.Industrial Electronic Technology Research GroupRand Afrikaans UniversityJohannesburgRepublic of South Africa

Personalised recommendations