Have a personal or library account? Click to login
Triangular Fuzzy-Rough Set Based Fuzzification of Fuzzy Rule-Based Systems Cover

Triangular Fuzzy-Rough Set Based Fuzzification of Fuzzy Rule-Based Systems

Journal of Artificial Intelligence and Soft Computing Research
Volume 10 (2020): Issue 4 (October 2020)
By: Janusz T. Starczewski,  Piotr Goetzen and  Christian Napoli  
Open Access
|Jun 2020

References

  1. [1] Almohammadi, K., Hagras, H., Alghazzawi, D., and Aldabbagh, G. Users-centric adaptive learning system based on interval type-2 fuzzy logic for massively crowded e-learning platforms. Journal of Artificial Intelligence and Soft Computing Research 6, 2 (2016), 81–101.10.1515/jaiscr-2016-0008
    Search in Google ScholarBack to article
  2. [2] Ferdaus, M. M., Anavatti, S. G., Garratt, M. A., and Pratam, M. Development of c-means clustering based adaptive fuzzy controller for a flapping wing micro air vehicle. Journal of Artificial Intelligence and Soft Computing Research 9, 2 (2019), 99–109.10.2478/jaiscr-2018-0027
    Search in Google ScholarBack to article
  3. [3] Greenfield, S., and Chiclana, F. Accuracy and complexity evaluation of defuzzification strategies for the discretised interval type-2 fuzzy set. International Journal of Approximate Reasoning 54, 8 (Oct 2013), 1013–1033.10.1016/j.ijar.2013.04.013
    Search in Google ScholarBack to article
  4. [4] Greenfield, S., Chiclana, F., Coupland, S., and John, R. The collapsing method of defuzzification for discretised interval type-2 fuzzy sets. Information Sciences 179, 13 (2009), 2055–2069.10.1016/j.ins.2008.07.011
    Search in Google ScholarBack to article
  5. [5] Han, Z.-q., Wang, J.-q., Zhang, H.-y., and Luo, X.-x. Group multi-criteria decision making method with triangular type-2 fuzzy numbers. International Journal of Fuzzy Systems 18, 4 (Aug 2016), 673–684.10.1007/s40815-015-0110-8
    Search in Google ScholarBack to article
  6. [6] Karnik, N. N., and Mendel, J. M. Centroid of a type-2 fuzzy set. Information Sciences 132 (2001), 195–220.10.1016/S0020-0255(01)00069-X
    Search in Google ScholarBack to article
  7. [7] Karnik, N. N., Mendel, J. M., and Liang, Q. Type-2 fuzzy logic systems. IEEE Transactions on Fuzzy Systems 7, 6 (1999), 643–658.10.1109/91.811231
    Search in Google ScholarBack to article
  8. [8] Liu, F. An efficient centroid type-reduction strategy for general type-2 fuzzy logic system. Information Sciences 178, 9 (2008), 2224–2236.10.1016/j.ins.2007.11.014
    Search in Google ScholarBack to article
  9. [9] Maowen Nie, and Woei Wan Tan. Towards an efficient type-reduction method for interval type-2 fuzzy logic systems. In 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence) (2008), pp. 1425–1432.10.1109/FUZZY.2008.4630559
    Search in Google ScholarBack to article
  10. [10] Melgarejo, M. A fast recursive method to compute the generalized centroid of an interval type-2 fuzzy set. In Proc. NAFIPS 2007 (2007), pp. 190–194.10.1109/NAFIPS.2007.383835
    Search in Google ScholarBack to article
  11. [11] Mendel, J. M., and Liu, X. Simplified interval type-2 fuzzy logic systems. IEEE Transactions on Fuzzy Systems 21, 6 (2013), 1056–1069.10.1109/TFUZZ.2013.2241771
    Search in Google ScholarBack to article
  12. [12] Mizumoto, M., and Tanaka, K. Some properties of fuzzy sets of type-2. Information and Control 31 (1976), 312–340.10.1016/S0019-9958(76)80011-3
    Search in Google ScholarBack to article
  13. [13] MonirVaghefi, H., Rafiee Sandgani, M., and Aliyari Shoorehdeli, M. Interval type-2 adaptive network-based fuzzy inference system (anfis) with type-2 non-singleton fuzzification. In 2013 13th Iranian Conference on Fuzzy Systems (IFSC) (2013), pp. 1–6.10.1109/IFSC.2013.6675612
    Search in Google ScholarBack to article
  14. [14] Monirvaghefi, H., and Shoorehdeli, M. A. Model-based fault detection of a nonlinear system using interval type-2 fuzzy systems with non-singleton type-2 fuzzification. In The 3rd International Conference on Control, Instrumentation, and Automation (2013), pp. 231–236.10.1109/ICCIAutom.2013.6912840
    Search in Google ScholarBack to article
  15. [15] Mouzouris, G. C., and Mendel, J. M. Nonsingleton fuzzy logic systems: theory and application. IEEE Transactions on Fuzzy Systems 5, 1 (1997), 56–71.10.1109/91.554447
    Search in Google ScholarBack to article
  16. [16] Nakamura, A. Fuzzy rough sets. Note on Multiple-Valued Logic in Japan 9, 8 (1988), 1–8.
    Search in Google ScholarBack to article
  17. [17] Nowicki, R. K., and Starczewski, J. T. A new method for classification of imprecise data using fuzzy rough fuzzification. Information Sciences 414 (2017), 33–52.10.1016/j.ins.2017.05.049
    Search in Google ScholarBack to article
  18. [18] Pekaslan, D., Wagner, C., and Garibaldi, J. M. Leveraging it2 input fuzzy sets in non-singleton fuzzy logic systems to dynamically adapt to varying uncertainty levels. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (2019), pp. 1–7.10.1109/FUZZ-IEEE.2019.8858800
    Search in Google ScholarBack to article
  19. [19] Pourabdollah, A., John, R., and Garibaldi, J. M. A new dynamic approach for non-singleton fuzzification in noisy time-series prediction. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (2017), pp. 1–6.10.1109/FUZZ-IEEE.2017.8015575
    Search in Google ScholarBack to article
  20. [20] Rojas, J. D., Salazar, O., and Serrano, H. Nie-Tan Method and its Improved Version: A Counterexample. IngenierÃa 21 (08 2016), 138 – 153.10.14483/udistrital.jour.reving.2016.2.a02
    Search in Google ScholarBack to article
  21. [21] Ruiz, G., Pomares, H., Rojas, I., and Hagras, H. The non-singleton fuzzification operation for general forms of interval type-2 fuzzy logic systems. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (2017), pp. 1–6.10.1109/FUZZ-IEEE.2017.8015414
    Search in Google ScholarBack to article
  22. [22] Sadiqbatcha, S., Jafarzadeh, S., and Ampatzidis, Y. Particle swarm optimization for solving a class of type-1 and type-2 fuzzy nonlinear equations. Journal of Artificial Intelligence and Soft Computing Research 8, 2 (2018), 103–110.10.1515/jaiscr-2018-0007
    Search in Google ScholarBack to article
  23. [23] Sahab, N., and Hagras, H. An adaptive type-2 input based nonsingleton type-2 fuzzy logic system for real world applications. In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011) (2011), pp. 509–516.10.1109/FUZZY.2011.6007680
    Search in Google ScholarBack to article
  24. [24] Starczewski, J. T. Efficient triangular type-2 fuzzy logic systems. International Journal of Approximate Reasoning 50 (2009), 799–811.10.1016/j.ijar.2009.03.001
    Search in Google ScholarBack to article
  25. [25] Starczewski, J. T. Extended triangular norms. Information Sciences 179 (2009), 742–757.10.1016/j.ins.2008.11.009
    Search in Google ScholarBack to article
  26. [26] Starczewski, J. T. General type-2 FLS with uncertainty generated by fuzzy rough sets. In Proc. IEEE-FUZZ 2010 (Barcelona, 2010), pp. 1790–1795.10.1109/FUZZY.2010.5584238
    Search in Google ScholarBack to article
  27. [27] Starczewski, J. T. Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty, vol. 284 of Studies in Fuzziness and Soft Computing. Springer, 2013.10.1007/978-3-642-29520-1
    Search in Google ScholarBack to article
  28. [28] Starczewski, J. T. Centroid of triangular and Gaussian type-2 fuzzy sets. Information Sciences 280 (2014), 289–306.10.1016/j.ins.2014.05.004
    Search in Google ScholarBack to article
  29. [29] Starczewski, J. T., Nowicki, R. K., and Nieszporek, K. Fuzzy-rough fuzzification in general FL classifiers. In Proceedings of the 11th International Joint Conference on Computational Intelligence, IJCCI 2019, Vienna, Austria, September 17-19, 2019 (2019), J. J. M. Guervós, J. Garibaldi, A. Linares-Barranco, K. Madani, and K. Warwick, Eds., ScitePress, pp. 335–342.10.5220/0008168103350342
    Search in Google ScholarBack to article
  30. [30] Wu, D., and Mendel, J. M. Enhanced karnik-mendel algorithms. IEEE Transactions on Fuzzy Systems 17, 4 (2009), 923–934.10.1109/TFUZZ.2008.924329
    Search in Google ScholarBack to article
  31. [31] Wu, D., and Tan, W. Computationally efficient type-reduction strategies for a type-2 fuzzy logic controller. In Proc. IEEE Fuzzy Conference (Reno, NV, 2005), pp. 353–358.
    Search in Google ScholarBack to article
  32. [32] Zadeh, L. A. The concept of a linguistic variable and its application to approximate reasoning — I. Information Sciences 8 (1975), 199–249.10.1016/0020-0255(75)90036-5
    Search in Google ScholarBack to article
  33. [33] Zhai, D., and Mendel, J. M. Centroid of a general type-2 fuzzy set computed by means of the centroid flow algorithm. In Proc. IEEE-FUZZ 2010 (Barcelona, 2010), pp. 1–8.10.1109/FUZZY.2010.5584547
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/jaiscr-2020-0018
Journal RSS Feed
Language: English
Page range: 271 - 285
Submitted on: Oct 3, 2019
Accepted on: May 1, 2020
Published on: Jun 15, 2020
Published by: SAN University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
general type-2 fuzzy logic systems,
fuzzy-rough fuzzification,
regular type-2 t-norms,
cropped triangular secondary membership functions
Related subjects:
Computer sciences,
Databases and data mining,
Artificial intelligence

© 2020 Janusz T. Starczewski, Piotr Goetzen, Christian Napoli, published by SAN University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Previous articleVolume 10 (2020): Issue 4 (October 2020)Next article