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Interval Type-3 Fuzzy Logic Systems (IT3FLS)

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Interval Type-3 Fuzzy Systems: Theory and Design

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 418))

Abstract

In this chapter we present the basic concepts and methods for building the Interval type-3 fuzzy systems.

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References

  1. Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice-Hall, Upper-Saddle River, NJ (2001)

    MATH  Google Scholar 

  2. Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10, 191–203 (1984)

    Article  Google Scholar 

  3. Pal, N.R., Bezdek, J.C.: On cluster validity for the fuzzy c-means model. IEEE Trans. Fuzzy Syst. 3(3), 370–379 (1995)

    Article  Google Scholar 

  4. Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell 13(8), 841–847 (1991)

    Article  Google Scholar 

  5. Yu, J., Cheng, Q., Huang, H.: Analysis of the weighting exponent in the fcm. IEEE Trans. Syst. Man Cybernetics-Part B 34(1), 634–639 (2004)

    Article  Google Scholar 

  6. B. Rust, StRD Dataset Gauss3 (1996)

    Google Scholar 

  7. Cao, Y., Raise, A., Mohammadzadeh, A., et al.: Deep learned recurrent type-3 fuzzy system: application for renewable energy modeling/prediction. Energy Reports (2021)

    Google Scholar 

  8. Mathworks, Inc., Natick, Massachusetts, Matlab Release 2013b (2013)

    Google Scholar 

  9. Dheeru, D., Karra Taniskidou, E.: {UCI} machine learning repository, Univ. Calif. Irvine Sch. Inf. (2017)

    Google Scholar 

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Correspondence to Oscar Castillo .

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Castillo, O., Castro, J.R., Melin, P. (2022). Interval Type-3 Fuzzy Logic Systems (IT3FLS). In: Interval Type-3 Fuzzy Systems: Theory and Design. Studies in Fuzziness and Soft Computing, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96515-0_4

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