Autors: Ahmed, S. A., Shakev, N. G., Topalov, A. V., Shiev K., Kaynak O.
Title: Sliding mode incremental learning algorithm for interval type-2 Takagi-Sugeno-Kang fuzzy neural networks
Keywords: Type-2 FL; Variable structure systems; Incremental learning

Abstract: Type-2 fuzzy logic systems are an area of growing interest over the last years. The ability to model uncertainties and to perform under noisy conditions in a better way than type-1 fuzzy logic systems increases their applicability. A new stable on-line learning algorithm for interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural networks is proposed in this paper. Differently from the other recently proposed variable structure system theorybased on-line learning approaches for the type-2 TSK fuzzy neural nets, where the adopted consequent part of the fuzzy rules consists solely of a constant, the developed algorithm applies the complete structure of the Takagi-Sugeno type fuzzy if-then rule base (i.e. first order instead of zero order output function is implemented). In addition it is able to adapt the existing relation between the lower and the upper membership functions of the type-2 fuzzy systems. This allows managing of non-uniform uncertainties.

References

    Issue

    Evolving Systems, vol. 3, issue 3, pp. 179-188, 2012, Germany, SPRINGER HEIDELBERGTIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY, DOI 10.1007/s12530-012-9053-6

    Цитирания (Citation/s):
    1. Precup, R.-E., Filip, H.-I., Rǎdac, M.-B., Pozna, C., Dragos, C.-A., Preitl, S. Experimental results of evolving Takagi - Sugeno fuzzy models for a nonlinear benchmark (2012) 3rd IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2012 - Proceedings, art. no. 6422044, pp. 567-572. DOI: 10.1109/CogInfoCom.2012.6422044 - 2012 - в издания, индексирани в Scopus или Web of Science
    2. Precup, R.-E., Radac, M.-B., Dragos, C.-A., Preitl, S., Petriu, E.M. Simulated annealing approach to fuzzy modeling of servo systems (2013) 2013 IEEE International Conference on Cybernetics, CYBCONF 2013, art. no. 6617449, pp. 267-272. DOI: 10.1109/CYBConf.2013.6617449 - 2013 - в издания, индексирани в Scopus или Web of Science
    3. Precup, R.-E., David, R.-C., Petriu, E.M., Preitl, S., Rǎdac, M.-B. Novel Adaptive Charged System Search algorithm for optimal tuning of fuzzy controllers (2014) Expert Systems with Applications, 41 (4 PART 1), pp. 1168-1175. DOI: 10.1016/j.eswa.2013.07.110 - 2014 - в издания, индексирани в Scopus или Web of Science
    4. Oniz, Y., Kaynak, O. Variable-structure-systems based approach for online learning of spiking neural networks and its experimental evaluation (2014) Journal of the Franklin Institute, 351 (6), pp. 3269-3285. DOI: 10.1016/j.jfranklin.2014.03.002 - 2014 - в издания, индексирани в Scopus или Web of Science
    5. Precup, R.-E., Filip, H.-I., Rədac, M.-B., Petriu, E.M., Preitl, S., Dragoş, C.-A. Online identification of evolving Takagi-Sugeno-Kang fuzzy models for crane systems (2014) Applied Soft Computing, 24, pp. 1155-1163. DOI: 10.1016/j.asoc.2014.01.013 - 2014 - в издания, индексирани в Scopus или Web of Science
    6. Pozna, C., Földesi, P., Precup, R.-E., Kóczy, L.T. On the development of signatures for Artificial Intelligence applications (2014) IEEE International Conference on Fuzzy Systems, art. no. 6891636, pp. 1304-1310. DOI: 10.1109/FUZZ-IEEE.2014.6891636 - 2014 - в издания, индексирани в Scopus или Web of Science
    7. Precup, R.-E., Sabau, M.-C., Petriu, E.M. Nature-inspired optimal tuning of input membership functions of Takagi-Sugeno-Kang fuzzy models for Anti-lock Braking Systems (2015) Applied Soft Computing, 27, pp. 575-589. DOI: 10.1016/j.asoc.2014.07.004 - 2015 - в издания, индексирани в Scopus или Web of Science
    8. Kayacan, E., Kayacan, E., Ahmadieh Khanesar, M. Identification of nonlinear dynamic systems using type-2 fuzzy neural networks - A novel learning algorithm and a comparative study (2015) IEEE Transactions on Industrial Electronics, 62 (3), art. no. 6871316, pp. 1716-1724. DOI: 10.1109/TIE.2014.2345353 - 2015 - в издания, индексирани в Scopus или Web of Science
    9. Oniz, Y., Kaynak, O. Control of a direct drive robot using fuzzy spiking neural networks with variable structure systems-based learning algorithm (2015) Neurocomputing, 149 (PB), pp. 690-699. DOI: 10.1016/j.neucom.2014.07.061 - 2015 - в издания, индексирани в Scopus или Web of Science
    10. Kayacan, E., Khanesar, M.A., Kayacan, E. Stabilization of type-2 fuzzy Takagi-Sugeno-Kang identifier using Lyapunov functions (2015) IEEE International Conference on Fuzzy Systems, 2015-November, art. no. 7337809, DOI: 10.1109/FUZZ-IEEE.2015.733780 - 2015 - в издания, индексирани в Scopus или Web of Science
    11. Benyounes, A., Hafaifa, A., Guemana, M. Gas Turbine Modeling Based on Fuzzy Clustering Algorithm Using Experimental Data (2016) Applied Artificial Intelligence, 30 (1), pp. 29-51. DOI: 10.1080/08839514.2016.1138808 - 2016 - в издания, индексирани в Scopus или Web of Science
    12. Sasu, L., Puiu, D., Nechifor, S. Fault recovery mechanism for smart city environments (2016) INES 2016 - 20th Jubilee IEEE International Conference on Intelligent Engineering Systems, Proceedings, art. no. 7555093, pp. 57-62. DOI: 10.1109/INES.2016.7555093 - 2016 - в издания, индексирани в Scopus или Web of Science
    13. Lin, C.-M., Le, T.-L., Huynh, T.-T. Self-evolving function-link interval type-2 fuzzy neural network for nonlinear system identification and control (2018) Neurocomputing, 275, pp. 2239-2250. DOI: 10.1016/j.neucom.2017.11.009 - 2018 - в издания, индексирани в Scopus или Web of Science
    14. 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
    15. Le, T.-L. Self-organizing recurrent interval type-2 Petri fuzzy design for time-varying delay systems (2019) IEEE Access, 7, art. no. 8585000, pp. 10505-10514. DOI: 10.1109/ACCESS.2018.2889226 - 2019 - в издания, индексирани в Scopus или Web of Science
    16. Le, T.-L. Intelligent fuzzy controller design for antilock braking systems (2019) Journal of Intelligent and Fuzzy Systems, 36 (4), pp. 3303-3315. DOI: 10.3233/JIFS-181014 - 2019 - в издания, индексирани в Scopus или Web of Science
    17. Khater, A.A., El-Nagar, A.M., El-Bardini, M., El-Rabaie, N. A Novel Structure of Actor-Critic Learning Based on an Interval Type-2 TSK Fuzzy Neural Network (2020) IEEE Transactions on Fuzzy Systems, 28 (11), art. no. 8884245, pp. 3047-3061. DOI: 10.1109/TFUZZ.2019.2949554 - 2020 - в издания, индексирани в Scopus или Web of Science
    18. Eyoh, I., Eyoh, J., Umoh, U., Kalawsky, R. Optimization of Interval Type-2 Intuitionistic Fuzzy Logic System for Prediction Problems (2021) International Journal of Computational Intelligence and Applications, 20 (4), art. no. 2150022, DOI: 10.1142/S146902682150022X - 2021 - в издания, индексирани в Scopus или Web of Science
    19. Eyoh, I.J., Adeoye, O.S., Inyang, U.G., Umoeka, I.J. A Hybrid Intelligent Parameter Tuning Approach for COVID-19 Time Series Modeling and Prediction (2022) Journal of Fuzzy Extension and Applications, 3 (1), pp. 64-80. DOI: 10.22105/jfea.2021.309332.1164 - 2022 - в издания, индексирани в Scopus или Web of Science
    20. Laha, M., Bose, D., Konar, A. TSK-Based Type-2 Fuzzy Analysis of Infrared Spectroscopic Data for Classification of Touch-Induced Affection (2023) Lecture Notes in Electrical Engineering, 985, pp. 147-162. DOI: 10.1007/978-981-19-8477-8_12 - 2023 - в издания, индексирани в Scopus или Web of Science
    21. Gharebaghi, S.M., Banazadeh, A. Identification and Robust Control of a Flapping Wing System Using Fuzzy Wavelet Neural Networks (2023) Unmanned Systems, DOI: 10.1142/S2301385025500207 - 2023 - в издания, индексирани в Scopus или Web of Science
    22. Gharebaghi S.M., Banazadeh A., Identification and Robust Control of a Flapping Wing System Using Fuzzy Wavelet Neural Networks (2025) Unmanned Systems, 13 (1), pp. 279 - 294, DOI: 10.1142/S2301385025500207 - 2025 - в издания, индексирани в Scopus или Web of Science

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