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Discrete Non-iterative Centroid Type-Reduction Algorithms on General Type-2 Fuzzy Logic Systems

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Abstract

Since the alpha-planes expressions of general type-2 fuzzy sets (GT2 FSs) have been proposed, general type-2 fuzzy logic systems (GT2 FLSs) that are dependent on GT2 FSs are becoming quite popular to fuzzy logic researchers. Usually enhanced Karnik–Mendel (EKM) algorithms are adopted for performing the kernel block of type-reduction (TR). However, the essence of EKM-based TR process probably hinders the GT2 FLSs from real-world applications. It is an intriguing as well as unsolved problem for comparing EKM algorithms with other non-iterative algorithms. This paper provides a framework encompassing fuzzy reasoning, defuzzification, as well as type-reduction. Furthermore, the continuous of NT (CNT) algorithm is shown to be a precise approach when it is used to execute the centroid TR of GT2 fuzzy logic systems. Four computer tests display the characteristics of discrete Nagar-Bardini (NB) and Nie-Tan (NT) non-iterative algorithms. Compared with EKM approach, the developed one exhibits some superiorities in terms of guaranteeing high computation accuracy and low computation burdens, which broadens the application ranges for the proposed method.

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References

  1. Khosravi, A., Nahavandi, S.: Load forecasting using interval type-2 fuzzy logic systems: optimal type reduction. IEEE Trans. Industr. Inf. 10(2), 1055–1063 (2014)

    Article  Google Scholar 

  2. Chen, Y., Wang, D.Z., Tong, S.C.: Forecasting studies by designing Mamdani interval type-2 fuzzy logic systems: with combination of BP algorithms and KM algorithms. Neurocomputing 174, 1133–1146 (2016)

    Article  Google Scholar 

  3. Melin, P., Astudillo, L., Castillo, O., Valdez, F., Garcia, M.: Optimal design of type-2 and type-1 fuzzy tracking controllers for autonomous mobile robots under perturbed torques using a new chemical optimization paradigm. Expert Syst. Appl. 40(8), 3185–3195 (2013)

    Article  Google Scholar 

  4. Barkat, S., Tlemcani, A., Nouri, H.: Noninteracting adaptive control of PMSM using interval type-2 fuzzy logic systems. IEEE Trans. Fuzzy Syst. 19(5), 925–936 (2011)

    Article  Google Scholar 

  5. Wang, D.Z., Chen, Y.: Study on permanent magnetic drive forecasting by designing Takagi Sugeno Kang type interval type-2 fuzzy logic systems. Trans. Inst. Measure. Control 40(6), 2011–2023 (2018)

    Article  Google Scholar 

  6. Bernardo, D., Hagras, H., Tsang, E.: A genetic type-2 fuzzy logic based system for the generation of summarized linguistic predictive models for financial applications. Soft. Comput. 17(12), 2185–2201 (2013)

    Article  Google Scholar 

  7. Lee, C.S., Wang, M.H., Hagras, H.: Type-2 fuzzy ontology and its application to personal diabetic-diet recommendation. IEEE Trans. Fuzzy Syst. 18(2), 316–328 (2010)

    Google Scholar 

  8. Méndez, G.M., Hernández, M.D.L.A.: Hybrid learning mechanism for interval A2–C1 type-2 non-singleton Takagi-Sugeno-Kang fuzzy logic systems. Inf. Sci. 220(1), 149–169 (2013)

    Article  Google Scholar 

  9. Mendoza, O., Melin, P., Castillo, O.: Interval type-2 fuzzy logic and modular networks for face recognition applications. Appl. Soft Comput. 9(4), 1377–1387 (2009)

    Article  Google Scholar 

  10. Niewiadomski, A.: On finity, countability, cardinalities, and cylindric extensions of type-2 fuzzy sets in linguistic summarization of databases. IEEE Trans. Fuzzy Syst. 18(3), 532–545 (2010)

    Article  Google Scholar 

  11. Wu, D.R., Mendel, J.M.: Uncertainty measures for interval type-2 fuzzy sets. Inf. Sci. 177(23), 5378–5393 (2007)

    Article  MathSciNet  Google Scholar 

  12. Mendel, J.M.: Alpha-plane representation for type-2 fuzzy sets: theory and applications. IEEE Trans. Fuzzy Syst. 17(5), 1189–1207 (2009)

    Article  Google Scholar 

  13. Mendel, J.M.: General type-2 fuzzy logic systems made simple: a tutorial. IEEE Trans. Fuzzy Syst. 22(5), 1162–1182 (2014)

    Article  Google Scholar 

  14. Mendel, J.M.: On KM algorithms for solving type-2 fuzzy set problems. IEEE Trans. Fuzzy Syst. 21(3), 426–446 (2013)

    Article  Google Scholar 

  15. Mendel, J.M., Liu, F.L.: Super-exponential convergence of the Karnik-Mendel algorithms for computing the centroid of an interval type-2 fuzzy set. IEEE Trans. Fuzzy Syst. 15(2), 309–320 (2007)

    Article  Google Scholar 

  16. Wu, D.R., Mendel, J.M.: Enhanced Karnik-Mendel algorithms. IEEE Trans. Fuzzy Syst. 17(4), 923–934 (2009)

    Article  Google Scholar 

  17. EI-Nagar, A.M., EI-Bardini, M.: Simplified interval type-2 fuzzy logic system based on new type-reduction. J. Intellig. Fuzzy Syst. 27(4), 1999–2010 (2014)

    Article  MathSciNet  Google Scholar 

  18. Li, J.W., John, R., Coupland, S., Kendall, G.: On Nie-Tan operator and type-reduction of interval type-2 fuzzy sets. IEEE Trans. Fuzzy Syst. 26(2), 1036–1039 (2018)

    Article  Google Scholar 

  19. Biglarbegian, M., Melek, W.W., Mendel, J.M.: On the robustness of type-1 and interval type-2 fuzzy logic systems in modeling. Inf. Sci. 181(7), 1325–1347 (2011)

    Article  MathSciNet  Google Scholar 

  20. Y. Chen. Study on centroid type-reduction of interval type-2 fuzzy logic systems based on noniterative algorithms. Complexity: Volume 2019. Article ID 7325053, 1–12 (2019)

    Google Scholar 

  21. Liu, F.L.: An efficient centroid type-reduction strategy for general type-2 fuzzy logic system. Inf. Sci. 178(9), 2224–2236 (2008)

    Article  MathSciNet  Google Scholar 

  22. Chen, Y., Wang, D.Z.: Forecasting by designing Mamdani general type-2 fuzzy logic systems optimized with quantum particle swarm optimization algorithms. Transactions of the Institute of Measurement and Control 41(10), 2886–2896 (2019)

    Article  MathSciNet  Google Scholar 

  23. Khanesar, M.A., Jalalian, A., Kaynak, O.: Improving the speed of center of set type-reduction in interval type-2 fuzzy systems by eliminating the need for sorting. IEEE Trans. Fuzzy Syst. 25(5), 1193–1206 (2017)

    Article  Google Scholar 

  24. Chen, Y.: Study on sampling based discrete Nie-Tan algorithms for computing the centroids of general type-2 fuzzy sets. IEEE Access 7(1), 156984–156992 (2019)

    Article  Google Scholar 

  25. Chen, Y., Wu, J.X., Lan, J.: Study on reasonable initialization enhanced Karnik-Mendel algorithms for centroid type-reduction of interval type-2 fuzzy logic systems. AIMS Mathematics 5(6), 6149–6168 (2020)

    Article  MathSciNet  Google Scholar 

  26. Ontiveros-Robles, E., Melin, P., Castillo, O.: Comparative analysis of noise robustness of type 2 fuzzy logic controllers. Kybernetika 54(1), 175–201 (2018)

    MathSciNet  MATH  Google Scholar 

  27. Sanchez, M.A., Castillo, O., Castro, J.R.: Generalized type-2 fuzzy systems for controlling a mobile robot and a performance comparison with interval type-2 and type-1 fuzzy systems. Expert Syst. Appl. 42(14), 5904–5914 (2015)

    Article  Google Scholar 

  28. Tong, S.C., Min, X., Li, Y.X.: Observer-based adaptive fuzzy tracking control for strict-feedback nonlinear systems with unknown control gain functions[J]. IEEE Transactions on Cybernetics 50(9), 3903–3913 (2020)

    Article  Google Scholar 

  29. Sun, W., Su, S.F., Wu, Y.Q., Xia, J.W., Nguyen, V.T.: Adaptive fuzzy control with high-order barrier Lyapunov functions for high-order uncertain nonlinear systems with full-state constraints[J]. IEEE Transactions on Cybernetics 50(8), 3424–3432 (2020)

    Article  Google Scholar 

  30. W. Sun, Y. Q. Wu, Z. Y. Sun. Command filter-based finite-time adaptive fuzzy control for uncertain nonlinear systems with prescribed performance. IEEE Trans. Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2020.2967295.

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Acknowledgements

This work is supported in part by the National Nature Science Foundation of China (No: 52070091), the Social Science Planning Foundation of Liaoning Province (No: L19BJY028), and the Innovation Talent Support Plan Project of Liaoning Province in 2019.

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Correspondence to Xiaomei Li.

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Li, X., Chen, Y. Discrete Non-iterative Centroid Type-Reduction Algorithms on General Type-2 Fuzzy Logic Systems. Int. J. Fuzzy Syst. 23, 704–715 (2021). https://doi.org/10.1007/s40815-020-00996-6

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  • DOI: https://doi.org/10.1007/s40815-020-00996-6

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