Abstract
General idea of the paper is comparison of different reasoning methods, which may be used in some types of fuzzy models. Different triangular norms and defuzzification methods were used. It is shown that many reasoning methods give similar results. However, many of them are not very reasonable. Some simple theorems about functions approximated by models are presented. Special attention is applied to modeling of physical processes. Examples of models used in reality are presented. Some of them are build as modifications of Takagi-Sugeno model introduced earlier by author.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Babuska R.: Fuzzy Modeling for Control, Kluwer Academic Publisher (1998).
Butkiewicz B. S.: Steady-State Error of a System with Fuzzy Controller, IEEE Transactions on System, Man, and Cybernetics, Part B: Cybernetics, Vol. 28, No. 6, (1998) 855–860.
Butkiewicz B. S.: About Robustness of Fuzzy Logic PD and PID Controller under Changes of Reasoning Methods, European Symp. on Intelligent Techniques, Aachen, Germany (2000) 350–356.
Butkiewicz B. S.:Fuzzy Reasoning Methods, its Properties and Applications (in polish), accepted for Prace Naukowe Politechniki Warszawskiej, Elektronika.
Butkiewicz B. S., Golovchak R., Kovalskiy A., Shpotyuk O., and Vakiv M.: On the Problem of Relaxation for Radiation-Induced Optical Effects in Some Ternary Chalcogenide Glasses, Radiation Effects and Deffects in Solids, (2000).
Czogala E., Leski J., On Equivalence of Approximate Reasoning Results Using Different Interpretations of Fuzzy if-then Rules, Fuzzy Sets and Systems, Vol. 117, (2001) 279–296.
Larsen P. M.: Industrial application of fuzzy logic control, Int. J. Man Machine Studies, Vol. 12, No. 1 (1980) 3–10.
Mamdani E. H.: Application of fuzzy algorithm for control of simple dynamic plant, Proc. IEE, Vol. 121, No. 12 (1974) 158–1588.
Mizumoto M., Toyoda J., Tanaka K.: General formulation of formal grammars, Information Science, Vol. 4 (1972) 87–100.
Pedrycz W.: Fuzzy Models: Methodology, Design, Applications and Challenges, in Perdycz W. (ed.) Fuzzy Modelling Paradigms end Practice, Kluwer Acad. Publ. pp 3–22, 1996.
Sugeno M., Kang G. T.: Structure Identification of Fuzzy Model, Fuzzy sets and Systems, Vol. 28 (1988) 15–33.
Takagi T., Sugeno M.: Fuzzy Identification of Systems and its Application to Modeling and Control, IEEE Trans. on Systems, Man, and Cybernetics, Vol. 15 (1985) 116–132.
Tsukamoto Y.: An approach to fuzzy reasoning method, in Fuzzy Set Theory and Applications, Gupta M. M., Ragade R. K., Yager R. R., (eds.), Amsterdam, North-Holland (1979).
Wang X. L.: Fuzzy Systems are Universal Approximators, Proc. IEEE Int. Conf. on Fuzzy Systems, San Diego, CA (1992) 1163–1169.
Ying H., Ding Y., Li S., Shao S.: Comparison of Necessary Conditions for Typical Takagi-Sugeno and Mamdani Fuzzy System as Universal Approximators, IEEE Trans. on Systems, Man, and Cybernetics-Part A, Vol. 29, No. 5 (1999) 508–514.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Butkiewicz, B.S. (2001). Inference in Fuzzy Models of Physical Processes. In: Reusch, B. (eds) Computational Intelligence. Theory and Applications. Fuzzy Days 2001. Lecture Notes in Computer Science, vol 2206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45493-4_77
Download citation
DOI: https://doi.org/10.1007/3-540-45493-4_77
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42732-2
Online ISBN: 978-3-540-45493-9
eBook Packages: Springer Book Archive