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Type-2 TSK Fuzzy Logic System and its Type-1 Counterpart

by Qun Ren, Marek Balazinski, Luc Baron
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 20 - Number 6
Year of Publication: 2011
Authors: Qun Ren, Marek Balazinski, Luc Baron
10.5120/2440-3292

Qun Ren, Marek Balazinski, Luc Baron . Type-2 TSK Fuzzy Logic System and its Type-1 Counterpart. International Journal of Computer Applications. 20, 6 ( April 2011), 8-13. DOI=10.5120/2440-3292

@article{ 10.5120/2440-3292,
author = { Qun Ren, Marek Balazinski, Luc Baron },
title = { Type-2 TSK Fuzzy Logic System and its Type-1 Counterpart },
journal = { International Journal of Computer Applications },
issue_date = { April 2011 },
volume = { 20 },
number = { 6 },
month = { April },
year = { 2011 },
issn = { 0975-8887 },
pages = { 8-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume20/number6/2440-3292/ },
doi = { 10.5120/2440-3292 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:07:02.651273+05:30
%A Qun Ren
%A Marek Balazinski
%A Luc Baron
%T Type-2 TSK Fuzzy Logic System and its Type-1 Counterpart
%J International Journal of Computer Applications
%@ 0975-8887
%V 20
%N 6
%P 8-13
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An interval type-2 TSK fuzzy logic system can be obtained by considering the membership functions of its existed type-1 counterpart as primary membership functions and assigning uncertainty to cluster centers, standard deviation of Gaussian membership functions and consequence parameters. In many cases it has been difficult to determine the spread percentages for these parameters to obtain an optimal model. In order to develop robust and reliable solutions for the problems, this paper distinguishes the differences between type-2 TSK system and its counterpart, analyzes the sensibility of the outputs of a type-2 TSK fuzzy system, and discusses the approximation capacities of type-2 TSK FLS and its type-1 counterpart as well.

References
  1. Takagi T., Sugeno M., 1985. Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. on Sys., Man, and Cyb., 15 (1), 116-132.
  2. Sugeno M. and Kang G., 1988. Structure identification of fuzzy model, Fuzzy Sets and Systems, 28 (1), 15-33.
  3. Johansen T., Foss B., 1995. Identification of non-linear system structure and parameters using regime decomposition, Automatic, 31 (2), 321–326.
  4. Zadeh L. A., 1965. Fuzzy sets, Information and Control, 8, 338-353.
  5. Zadeh L. A., 1975. The conception of a linguistic variable and its application in approximate reasoning – I, Information Science. 8, 199-249.
  6. Mendel J. M., 2001. Uncertain rule-based fuzzy logic systems – introduction on new directions, Prentice hall PTR, upper saddle river, NJ.
  7. Liang Q. and Mendel J. M., 1999. An introduction to type-2 TSK fuzzy logic systems, 1999 IEEE International Systems Conference Processing, Seoul, Korea.
  8. Zadeh L. A., 1968. Fuzzy algorithm, Inf. and Contr., 12, 94-102.
  9. Zadeh L. A., 1973. Outline of a new approach to the analysis of complex systems and decision processes, IEEE Trans. on Sys., Man, and Cyb., 3, 28-44.
  10. Mamdani E. H. and Assilian S., 1974, Applications of fuzzy algorithms for control of simple dynamic plant, Proc. Inst. Elec. Eng., 121, 1585-1588.
  11. Mendel J. M., Hagras H., John R. I., 2006. Standard background material about interval type-2 fuzzy logic systems that can be used by all authors, http://ieee-cis.org/_files/standards.t2.win.pdf.
  12. Ren Q., Baron L. and Balazinski M., 2006. Type-2 Takagi-Sugeno-Kang fuzzy logic modeling using subtractive clustering, In Proceeding of the 25th north American Fuzzy Information Processing Society Annual Conference (NAFIPS 2006), Montreal, Canada,1-6.
  13. Wang L. –X and Mendel J. M., 1992. Fuzzy basis functions, universal approximation, and orthogonal least squares learning. IEEE Transaction on Neural Networks, 3, 807-813.
  14. Ren Q., Baron L., Balazinski M. and Jemielniak K., 2011. TSK fuzzy modeling for tool wear condition in turning processes: an experimental study. Engineering Applications of Artificial Intelligence, 24(2), 260-265. doi:10.1016/j.engappai.2010.10.016
  15. Ren Q., Baron L., Balazinski M. and Jemielniak K.,2008. Tool condition monitoring using the TSK fuzzy approach based on subtractive clustering method, News Frontiers in Applied Artificial Intelligence, 52-61, Springer-Verlag, Berlin.
  16. Ren Q., Baron L. and Balazinski M., 2009. Uncertainty prediction for tool wear condition using type-2 TSK fuzzy approach. In Proceeding of the 2009 IEEE International Conference on Systems, Man, and Cybernetics (IEEE-SMC 2009), San Antonio, TX, USA, 666 - 671.
  17. Ren Q., Baron L., Jemielniak K. and Balazinski M., 2010. Modeling of dynamic micromilling cutting forces using type-2 fuzzy rule-based system. In Proceeding of 2010 IEEE World Congress on Computational Intelligence International Conference on Fuzzy Systems (WCCI 2010, FUZZ-IEEE 2010), Barcelona, Spain, 2313-2317.
  18. Ren Q., Baron L., Jemielniak K. and Balazinski M., 2010. Acoustic emission signal feature analysis using type-2 fuzzy logic system. In The 29th North American Fuzzy Information Processing Society Annual Conference (NAFIPS 2010), Toronto, Canada, 1-6.
Index Terms

Computer Science
Information Sciences

Keywords

fuzzy logic system membership functions uncertainty sensibility capability

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