Autors: Shiev K. B., Shakev, N. G., Topalov, A. V., Ahmed, S. A., Kaynak O.
Title: An extended sliding mode learning algorithm for type-2 fuzzy neural networks
Keywords: type-2 fuzzy logic systems, variable structure systems

Abstract: Type-2 fuzzy logic systems are an area of growing interest over the last years. The ability to model uncertainties in a better way than type-1 fuzzy logic systems increases their applicability. A new stable on-line learning algorithm for type-2 fuzzy neural networks is proposed in this paper. It can be considered as an extended version of the recently developed on-line learning approaches for type-2 fuzzy neural networks based on the Variable Structure System theory concepts. Simulation results from the identification of a nonlinear system with uncertainties have demonstrated the better performance of the proposed extended algorithm in comparison with the previously reported in the literature sliding mode learning algorithms for both type-1 and type-2 fuzzy neural structures.

References

    Issue

    2nd International Conference on Adaptive and Intelligent Systems, Published in Springer Lecture Notes in Artificial Intelligence, vol. 6943, pp. 52-63, 2011, Austria, SPRINGER-VERLAG BERLINHEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY, ISSN 0302-9743

    Цитирания (Citation/s):
    1. Khanesar, M.A., Mendel, J.M. Maclaurin series expansion complexity-reduced center of sets type-reduction + defuzzification for interval type-2 fuzzy systems (2016) 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, art. no. 07737828, pp. 1224-1231. DOI: 10.1109/FUZZ-IEEE.2016.7737828 - 2016 - в издания, индексирани в Scopus или Web of Science
    2. Hamza, M.F., Yap, H.J., Choudhury, I.A., Chiroma, H., Kumbasar, T. A survey on advancement of hybrid type 2 fuzzy sliding mode control (2018) Neural Computing and Applications, 30 (2), pp. 331-353. DOI: 10.1007/s00521-017-3144-z - 2018 - в издания, индексирани в Scopus или Web of Science

    Вид: записки от лекции, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science