A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
O. Castillo, F. Valdez, P. Melin, and W. Ding, “A survey on type-3 fuzzy logic systems and their control applications,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 8, pp. 1–13, Aug. 2024. doi: 10.1109/JAS.2024.124530
Citation: O. Castillo, F. Valdez, P. Melin, and W. Ding, “A survey on type-3 fuzzy logic systems and their control applications,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 8, pp. 1–13, Aug. 2024. doi: 10.1109/JAS.2024.124530

A Survey on Type-3 Fuzzy Logic Systems and Their Control Applications

doi: 10.1109/JAS.2024.124530
Funds:  The authors would like to thank CONAHCYT and Tecnológico Nacional de Mexico/Tijuana Institute of Technology for the support during this research
More Information
  • In this paper, we offer a review o.lpe-3 fuzzy logic systems and their applications in control. The main objective of this work is to observe and analyze in detail the applications in the control area using type-3 fuzzy logic systems. In this case, we review their most important applications in control and other related topics with type-3 fuzzy systems. Intelligent algorithms have been receiving increasing attention in control and for this reason a review in this area is important. This paper reviews the main applications that make use of Intelligent Computing methods. Specifically, type-3 fuzzy logic systems. The aim of this research is to be able to appreciate, in detail, the applications in control systems and to point out the scientific trends in the use of Intelligent Computing techniques. This is done with the construction and visualization of bibliometric networks, developed with VosViewer Software, which it is a free Java-based program, mainly intended to be used for analyzing and visualizing bibliometric networks. With this tool, we can create maps of publications, authors, or journals based on a co-citation network or construct maps of keywords, countries based on a co-occurrence networks, research groups, etc.

     

  • loading
  • [1]
    C. Moraga, “Introduction to fuzzy logic,” Ser.: Elec. Energ., vol. 18, no. 2, pp. 319–328, Aug. 2005.
    [2]
    L. A. Zadeh, “Fuzzy sets,” Inf. Control, vol. 8, no. 3, pp. 338–353, Jun. 1965. doi: 10.1016/S0019-9958(65)90241-X
    [3]
    Y. Disser, O. Friedmann, and A. V. Hopp, “An exponential lower bound for Zadeh’s pivot rule,” Mathematical Programming, vol. 199, no. 1–2, pp. 865–936, May 2003.
    [4]
    G. J. Klir and B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications. New Jersey, USA: Prentice Hall, 1995.
    [5]
    T. J. Ross, Fuzzy Logic With Engineering Applications. 3rd ed. Chichester, UK: John Wiley & Sons, Ltd., 2010, pp. 606.
    [6]
    C. Moraga, “A metasemantic interpretation of mamdani systems,” in Enric Trillas: A Passion for Fuzzy Sets: A Collection of Recent Works on Fuzzy Logic, L. Magdalena, J. L. Verdegay, and F. Esteva, Eds. Cham, Germany: Springer, 2015, pp. 167–178.
    [7]
    P. P. Bonissone, “Fuzzy logic and soft computing: Technology development and applications,” General Electric CRD, Schenectady, USA, 1997.
    [8]
    C. Carlsson, “Introduction to fuzzy logic and soft computing in service and management support minitrack,” in Proc. 45th Hawaii Int. Conf. System Sciences, Maui, USA, 2012, pp. 1117.
    [9]
    O. Castillo, P. Melin, O. Montiel Ross, R. S. Cruz, W. Pedrycz, and J. Kacprzyk, Theoretical Advances and Applications of Fuzzy Logic and Soft Computing. Berlin, Germany: Springer-Verlag, 2007, pp. 69–83.
    [10]
    D. Dubois, R. Martin-Clouaire, and H. Prade, “Practical computing in fuzzy logic,” in Fuzzy Computing, M. M. Gupta and T. Yamakawa, Eds. North-Holland, USA: Elsevier, 1988, pp. 11–34.
    [11]
    E. Bernal, M. L. Lagunes, O. Castillo, J. Soria, and F. Valdez, “Optimization of type-2 fuzzy logic controller design using the GSO and FA algorithms,” Int. J. Fuzzy Syst., vol. 23, no. 1, pp. 42–57, Feb. 2021. doi: 10.1007/s40815-020-00976-w
    [12]
    B. I. TÜrkşen, Recent advances in fuzzy system modeling,” in Frontiers of Higher Order Fuzzy Sets, A. Sadeghian and H. Tahayori, Eds. New York, USA: Springer, 2015, pp. 51–66.
    [13]
    O. Castillo, J. R. Castro, and P. Melin, “Interval type-3 fuzzy logic systems (IT3FLS),” in Interval Type-3 Fuzzy Systems: Theory and Design, O. Castillo, J. R. Castro, and P. Melin, Eds. Cham, Germany: Springer, 2022, pp. 45–98.
    [14]
    O. Elhaki, K. Shojaei, and A. Mohammadzadeh, “Robust state and output feedback prescribed performance interval type-3 fuzzy reinforcement learning controller for an unmanned aerial vehicle with actuator saturation,” IET Control Theory Appl., vol. 17, no. 5, pp. 605–627, Mar. 2023. doi: 10.1049/cth2.12415
    [15]
    S. H. H. Shah, S. Lei, M. Ali, D. Doronin, and S. T. Hussain, “Prosumption: Bibliometric analysis using HistCite and VOSviewer,” Kybernetes, vol. 49, no. 3, pp. 1020–1045, Feb. 2020.
    [16]
    M. R. Amiri Shahmirani, A. A. Nikghalb Rashti, M. R. Adib Ramezani, and E. M. Golafshani, “Buildings, causalities, and injuries innovative fuzzy damage model during earthquakes,” Shock Vib., vol. 2022, p. 4746587, Jul. 2022.
    [17]
    Y. J. Ryoo, “Trajectory-tracking control of a transport robot for smart logistics using the fuzzy controller,” Int. J. Fuzzy Log. Intell. Syst., vol. 22, no. 1, pp. 69–77, Mar. 2022. doi: 10.5391/IJFIS.2022.22.1.69
    [18]
    V. Suganya, P. G. Sundari, and N. Rajesh, “Double fuzzy-irresolute multifunctions,” Int. J. Fuzzy Log. Intell. Syst., vol. 22, no. 1, pp. 100–105, Mar. 2022. doi: 10.5391/IJFIS.2022.22.1.100
    [19]
    R. Raouf and S. Elsaieed, “Using fuzzy time series models to estimate the cost of benefits of Egyptian social insurance system,” Int. J. Fuzzy Log. Intell. Syst., vol. 22, no. 2, pp. 169–182, Jun. 2022. doi: 10.5391/IJFIS.2022.22.2.169
    [20]
    D. Behera and S. Chakraverty, “Computational technique for solving imprecisely defined non-negative fully fuzzy algebraic system of linear equations,” Int. J. Fuzzy Log. Intell. Syst., vol. 22, no. 3, pp. 252–260, Sep. 2022. doi: 10.5391/IJFIS.2022.22.3.252
    [21]
    B. Veerasamy and C. M. Sangeetha, “MB-FL: Macro-block fuzzy logic for video compression in multimedia applications,” Int. J. Fuzzy Log. Intell. Syst., vol. 22, no. 4, pp. 366–372, Dec. 2022. doi: 10.5391/IJFIS.2022.22.4.366
    [22]
    D. Wu, R. Peng, and J. M. Mendel, “Type-1 and interval type-2 fuzzy systems [AI-explained],” IEEE Comput. Intell. Mag., vol. 18, no. 1, pp. 81–83, Feb. 2023. doi: 10.1109/MCI.2022.3223496
    [23]
    A. Sakalli, T. Kumbasar, and J. M. Mendel, “Towards systematic design of general type-2 fuzzy logic controllers: Analysis, interpretation, and tuning,” IEEE Trans. Fuzzy Syst., vol. 29, no. 2, pp. 226–239, Feb. 2021. doi: 10.1109/TFUZZ.2020.3016034
    [24]
    J. M. Mendel, I. Eyoh, and R. John, “Comparing performance potentials of classical and intuitionistic fuzzy systems in terms of sculpting the state space,” IEEE Trans. Fuzzy Syst., vol. 28, no. 9, pp. 2244–2254, Sep. 2020. doi: 10.1109/TFUZZ.2019.2933786
    [25]
    J. M. Mendel, “Explaining the performance potential of rule-based fuzzy systems as a greater sculpting of the state space,” IEEE Trans. Fuzzy Syst., vol. 26, no. 4, pp. 2362–2373, Aug. 2018. doi: 10.1109/TFUZZ.2017.2774190
    [26]
    Y. Kawano, F. Valdez, and Ó. Castillo, “Fuzzy combination of moth-flame optimization and lightning search algorithm with fuzzy dynamic parameter adjustment,” Comput. Sist., vol. 26, no. 2, pp. 743–757, Jun. 2022.
    [27]
    F. Valdez, Y. Kawano, and P. Melin, “Toward the best combination of optimization with fuzzy systems to obtain the best solution for the GA and PSO algorithms using parallel processing,” J. Autom. Mob. Rob. Intell. Syst., vol. 14, no. 1, pp. 55–64, Jul. 2020.
    [28]
    P. Prakash and Y. H. Joo, “Fuzzy-logic-based event-triggered H control for networked systems and its application to wind turbine systems,” Inf. Sci., vol. 585, pp. 144–161, Mar. 2022. doi: 10.1016/j.ins.2021.11.039
    [29]
    T. Hou, N. Li, Y. Wang, K. Pang, and L. Zou, “(α, β)-colored resolution method of linguistic truth-valued intuitionistic fuzzy logic,” in Proc. 2nd Int. Conf., Shanghai, China, 2022, pp. 79–90.
    [30]
    F. Valdez, O. Castillo, and P. Melin, “A review on type-2 fuzzy systems in robotics and prospects for type-3 fuzzy,” in Proc. ICAMCI-2020, Tripura, India, 2023, 211–223.
    [31]
    N. J. van Eck and L. Waltman, “Software survey: VOSviewer, a computer program for bibliometric mapping,” Scientometrics, vol. 84, no. 2, pp. 523–538, Aug. 2010. doi: 10.1007/s11192-009-0146-3
    [32]
    A. Taghieh, A. A. Aly, B. F. Felemban, A. Althobaiti, A. Mohammadzadeh, and A. Bartoszewicz, “A hybrid predictive type-3 fuzzy control for time-delay multi-agent systems,” Electronics, vol. 11, no. 1, p. 63, Dec. 2022.
    [33]
    O. Castillo, J. R. Castro, M. Pulido, and P. Melin, “Interval type-3 fuzzy aggregators for ensembles of neural networks in COVID-19 time series prediction,” Eng. Appl. Artif. Intell., vol. 114, p. 105110, Sep. 2022. doi: 10.1016/j.engappai.2022.105110
    [34]
    A. Taghieh, A. Mohammadzadeh, C. Zhang, N. Kausar, and O. Castillo, “A type-3 fuzzy control for current sharing and voltage balancing in microgrids,” Appl. Soft Comput., vol. 129, p. 109636, Nov. 2022. doi: 10.1016/j.asoc.2022.109636
    [35]
    O. Castillo and P. Melin, “Interval type-3 fuzzy decision making in material surface quality control,” in Hybrid Intelligent Systems Based on Extensions of Fuzzy Logic, Neural Networks and Metaheuristics, O. Castillo and P. Melin, Eds. Cham: Germany, Springer, 2023, pp. 479–487.
    [36]
    L. Zhao, G. Yang, Y. Li, and X. Hu, “Fuzzy adaptive optimal backstepping control of the FO MEMS resonator under imprecise target trajectory with disturbance compensation mechanism,” Nonlinear Dyn., vol. 111, no. 19, pp. 17939–17959, Oct. 2023. doi: 10.1007/s11071-023-08744-9
    [37]
    J. Wang, H. Dong, F. Chen, M. T. Vu, A. D. Shakibjoo, and A. Mohammadzadeh, “Formation control of non-holonomic mobile robots: Predictive data-driven fuzzy compensator,” Mathematics, vol. 11, no. 8, p. 1804, Apr. 2023. doi: 10.3390/math11081804
    [38]
    M. A. Balootaki, H. Rahmani, H. Moeinkhah, and A. Mohammadzadeh, “Non-singleton fuzzy control for multi-synchronization of chaotic systems,” Appl. Soft Comput., vol. 99, p. 106924, Feb. 2021. doi: 10.1016/j.asoc.2020.106924
    [39]
    J.-H. Wang, J. Tavoosi, A. Mohammadzadeh, S. Mobayen, J. H. Asad, W. Assawinchaichote, M. T. Vu, and P. Skruch, “Non-singleton type-3 fuzzy approach for flowmeter fault detection: Experimental study in a gas industry,” Sensors, vol. 21, no. 21, p. 7419, Nov. 2021. doi: 10.3390/s21217419
    [40]
    Z. Liu, A. Mohammadzadeh, H. Turabieh, M. Mafarja, S. S. Band, and A. Mosavi, “A new online learned interval type-3 fuzzy control system for solar energy management systems,” IEEE Access, vol. 9, pp. 10498–10508, Jan. 2021. doi: 10.1109/ACCESS.2021.3049301
    [41]
    G. M. Mendez, I. Lopez-Juarez, P. N. Montes-Dorantes, and M. A. Garcia, “A new method for the design of interval type-3 fuzzy logic systems with uncertain type-2 non-singleton inputs (IT3 NSFLS-2): A case study in a hot strip mill,” IEEE Access, vol. 11, pp. 44065–44081, Jan. 2023. doi: 10.1109/ACCESS.2023.3272531
    [42]
    A. Taghieh, C. Zhang, K. A. Alattas, Y. Bouteraa, S. Rathinasamy, and A. Mohammadzadeh, “A predictive type-3 fuzzy control for underactuated surface vehicles,” Ocean Eng., vol. 266, p. 113014, Dec. 2022. doi: 10.1016/j.oceaneng.2022.113014
    [43]
    L. Amador-Angulo, O. Castillo, J. R. Castro, and P. Melin, “A new approach for interval type-3 fuzzy control of nonlinear plants,” Int. J. Fuzzy Syst., vol. 25, no. 4, pp. 1624–1642, Jun. 2023. doi: 10.1007/s40815-023-01470-9
    [44]
    A. Mohammadzadeh and R. H. Vafaie, “A deep learned fuzzy control for inertial sensing: Micro electro mechanical systems,” Appl. Soft Comput., vol. 109, p. 107597, Sep. 2021. doi: 10.1016/j.asoc.2021.107597
    [45]
    S. Xu, C. Zhang, and A. Mohammadzadeh, “Type-3 fuzzy control of robotic manipulators,” Symmetry, vol. 15, no. 2, p. 483, Feb. 2023. doi: 10.3390/sym15020483
    [46]
    A. Xu, M.-W. Tian, N. Kausar, A. Mohammadzadeh, D. Pamucar, and E. Ozbilge, “Optimal type-3 fuzzy control and analysis of complicated financial systems,” J. Intell. Fuzzy Syst. Appl. Eng. Technol., vol. 44, no. 5, pp. 7121–7134, Jan. 2023.
    [47]
    O. Castillo, J. R. Castro, and P. Melin, “Interval type-3 fuzzy fractal approach in sound speaker quality control evaluation,” Eng. Appl. Artif. Intell., vol. 116, p. 105363, Nov. 2022. doi: 10.1016/j.engappai.2022.105363
    [48]
    A. Fletcher and J. P. Davis, “Decision-making with incomplete evidence,” in Proc. SPE Asia Pacific Oil and Gas Conf. and Exhibition, Melbourne, Australia, 2002, pp. 725–739.
    [49]
    S. Kousar, T. Saleem, N. Kausar, D. Pamucar, and G. M. Addis, “Homomorphisms of lattice-valued intuitionistic fuzzy subgroup type-3,” Comput. Intell. Neurosci., vol. 2022, p. 6847138, Apr. 2022.
    [50]
    C. Peraza, P. Ochoa, O. Castillo, and Z. W. Geem, “Interval-type 3 fuzzy differential evolution for designing an interval-type 3 fuzzy controller of a unicycle mobile robot,” Mathematics, vol. 10, no. 19, p. 3533, Sep. 2022. doi: 10.3390/math10193533
    [51]
    D. J. Singh, N. K. Verma, A. K. Ghosh, and A. Malagaudanavar, “An approach towards the design of interval type-3 T-S fuzzy system,” IEEE Trans. Fuzzy Syst., vol. 30, no. 9, pp. 3880–3893, Sep. 2022. doi: 10.1109/TFUZZ.2021.3133083
    [52]
    A. Xu, K. A. Alattas, N. Kausar, A. Mohammadzadeh, E. Ozbilge, and T. Cagin, “A non-singleton type-3 fuzzy modeling: Optimized by square-root cubature Kalman filter,” Intell. Autom. Soft Comput., vol. 37, no. 1, pp. 17–32, Mar. 2023. doi: 10.32604/iasc.2023.036623
    [53]
    G. Hua, F. Wang, J. Zhang, K. A. Alattas, A. Mohammadzadeh, and M. T. Vu, “A new type-3 fuzzy predictive approach for mobile robots,” Mathematics, vol. 10, no. 17, p. 3186, Sep. 2022. doi: 10.3390/math10173186
    [54]
    W. Fan, A. Mohammadzadeh, N. Kausar, D. Pamucar, and N. Al Din Ide, “A new type-3 fuzzy PID for energy management in microgrids,” Adv. Math. Phys., vol. 2022, p. 8737448, Jul. 2022.
    [55]
    P. Aazagreyir, P. Appiahene, O. Appiah, S. Boateng, W. L. Brown-Acquaye, and G. Y. Koi-Akrofi, “An integrated fuzzy multi-criteria decision-making methods for service selection: A systematic literature review and meta-analysis,” J. Theor. Appl. Inf. Technol., vol. 100, no. 15, pp. 4671–4697, Aug. 2022.
    [56]
    O. Castillo and P. Melin, “Towards interval type-3 intuitionistic fuzzy sets and systems,” Mathematics, vol. 10, no. 21, p. 4091, Nov. 2022. doi: 10.3390/math10214091
    [57]
    P. Melin, D. Sánchez, J. R. Castro, and O. Castillo, “Design of type-3 fuzzy systems and ensemble neural networks for COVID-19 time series prediction using a firefly algorithm,” Axioms, vol. 11, no. 8, p. 410, Aug. 2022. doi: 10.3390/axioms11080410
    [58]
    P. Ochoa, O. Castillo, P. Melin, and J. R. Castro, “Interval type-3 fuzzy differential evolution for parameterization of fuzzy controllers,” Int. J. Fuzzy Syst., vol. 25, no. 4, pp. 1360–1376, Jun. 2023. doi: 10.1007/s40815-022-01451-4
    [59]
    A. Tarafdar, P. Majumder, M. Deb, and U. K. Bera, “Application of a q-rung orthopair hesitant fuzzy aggregated type-3 fuzzy logic in the characterization of performance-emission profile of a single cylinder CI-engine operating with hydrogen in dual fuel mode,” Energy, vol. 269, p. 126751, Apr. 2023. doi: 10.1016/j.energy.2023.126751
    [60]
    C. Ma, A. Mohammadzadeh, H. Turabieh, M. Mafarja, S. S. Band, and A. Mosavi, “Optimal type-3 fuzzy system for solving singular multi-pantograph equations,” IEEE Access, vol. 8, pp. 225692–225702, Dec. 2020. doi: 10.1109/ACCESS.2020.3044548
    [61]
    A. Garrido, Emergent tools in AI,” in Artificial Intelligence: Approaches, Tools and Applications, B. M. Gordon, Ed. New York, USA: Nova Science Publishers, Inc., 2011.
    [62]
    O. Castillo, J. R. Castro, and P. Melin, “Interval type-3 fuzzy systems: A natural evolution from type-1 and type-2 fuzzy systems,” in Fuzzy Logic and Neural Networks for Hybrid Intelligent System Design, O. Castillo and P. Melin, Eds. Cham, Germany: Springer, 2023, pp. 209–221.
    [63]
    M. Gheisarnejad, A. Mohammadzadeh, and M.-H. Khooban, “Model predictive control based type-3 fuzzy estimator for voltage stabilization of dc power converters,” IEEE Trans. Ind. Electron., vol. 69, no. 12, pp. 13849–13858, Dec. 2022. doi: 10.1109/TIE.2021.3134052
    [64]
    O. Castillo, M. Pulido, and P. Melin, “Interval type-3 fuzzy aggregators for ensembles of neural networks in time series prediction,” in Intelligent and Fuzzy Systems, C. Kahraman, A. C. Tolga, S. C. Onar, S. Cebi, B. Oztaysi, and I. U. Sari, Eds. Cham, Germany: Springer, 2022, pp. 785–793.
    [65]
    S. N. Qasem, A. Ahmadian, A. Mohammadzadeh, S. Rathinasamy, and B. Pahlevanzadeh, “A type-3 logic fuzzy system: Optimized by a correntropy based Kalman filter with adaptive fuzzy kernel size,” Inf. Sci., vol. 572, pp. 424–443, Sep. 2021. doi: 10.1016/j.ins.2021.05.031
    [66]
    H.-S. Choi and K.-W. Lee, “The tuning method on consequence membership function of t-s type FLC,” J. Inst. Control Rob. Syst., vol. 17, no. 3, pp. 264–268, Mar. 2011. doi: 10.5302/J.ICROS.2011.17.3.264
    [67]
    B. Yildirim, M. Gheisarnejad, A. Mohammadzadeh, and M. H. Khooban, “Intelligent frequency stabilization of low-inertia islanded power grids-based redox battery,” J. Energy Storage, vol. 71, p. 108190, Nov. 2023. doi: 10.1016/j.est.2023.108190
    [68]
    H. Ying, “Structure and stability analysis of general mamdani fuzzy dynamic models,” Int. J. Intell. Syst., vol. 20, no. 1, pp. 103–125, Jan. 2005. doi: 10.1002/int.20056
    [69]
    C. Peraza, O. Castillo, P. Melin, J. R. Castro, J. H. Yoon, and Z. W. Geem, “A type-3 fuzzy parameter adjustment in harmony search for the parameterization of fuzzy controllers,” Int. J. Fuzzy Syst., vol. 25, no. 6, pp. 2281–2294, Sep. 2023. doi: 10.1007/s40815-023-01499-w
    [70]
    B. Yan, X. Jiang, K. A. Alattas, C. Zhang, and A. Mohammadzadeh, “Generation of limit cycles in nonlinear systems: Machine leaning based type-3 fuzzy control,” IEEE Access, vol. 11, pp. 34835–34845, Jan. 2023. doi: 10.1109/ACCESS.2023.3264801
    [71]
    A. Tarafdar, P. Majumder, M. Deb, and U. K. Bera, “Performance-emission optimization in a single cylinder ci-engine with diesel hydrogen dual fuel: A spherical fuzzy MARCOS MCGDM based type-3 fuzzy logic approach,” Int. J. Hyd. Energy, vol. 48, no. 73, pp. 28601–28627, Aug. 2023. doi: 10.1016/j.ijhydene.2023.04.019
    [72]
    H. Huang, H. Xu, F. Chen, C. Zhang, and A. Mohammadzadeh, “An applied type-3 fuzzy logic system: Practical Matlab Simulink and M-Files for robotic, control, and modeling applications,” Symmetry, vol. 15, no. 2, p. 475, Feb. 2023. doi: 10.3390/sym15020475
    [73]
    O. Elhaki, K. Shojaei, A. Mohammadzadeh, and S. Rathinasamy, “Robust amplitude-limited interval type-3 neuro-fuzzy controller for robot manipulators with prescribed performance by output feedback,” Neural Comput. Appl., vol. 35, no. 12, pp. 9115–9130, Apr. 2023.
    [74]
    F. Cassalho, S. Beskow, C. R. de Mello, M. M. de Moura, L. F. de Oliveira, and M. S. de Aguiar, “Artificial intelligence for identifying hydrologically homogeneous regions: A state-of-the-art regional flood frequency analysis,” Hydrol. Processes, vol. 33, no. 7, pp. 1101–1116, Mar. 2019. doi: 10.1002/hyp.13388
    [75]
    M. Hamdy, A. Ibrahim, B. Abozalam, and S. Helmy, “Maximum power point tracking for solar photovoltaic system based on interval type-3 fuzzy logic: Practical validation,” Electr. Power Compon. Syst., vol. 51, no. 10, pp. 1009–1026, Mar. 2023. doi: 10.1080/15325008.2023.2188316
    [76]
    O. Castillo, J. R. Castro, and P. Melin, “Interval type-3 fuzzy aggregation of neural networks for multiple time series prediction: The case of financial forecasting,” Axioms, vol. 11, no. 6, p. 251, May 2022. doi: 10.3390/axioms11060251
    [77]
    R. H. Vafaie, A. Mohammadzadeh, and M. J. Piran, “A new type-3 fuzzy predictive controller for MEMS gyroscopes,” Nonlinear Dyn., vol. 106, no. 1, pp. 381–403, Sep. 2021. doi: 10.1007/s11071-021-06830-4
    [78]
    A. Riaz, S. Kousar, N. Kausar, D. Pamucar, and G. M. Addis, “An analysis of algebraic codes over lattice valued intuitionistic fuzzy type-3 R-submodules,” Comput. Intell. Neurosci., vol. 2022, p. 8148284, Jun. 2022.
    [79]
    A. Mohammadzadeh, O. Castillo, S. S. Band, and A. Mosavi, “A novel fractional-order multiple-model type-3 fuzzy control for nonlinear systems with unmodeled dynamics,” Int. J. Fuzzy Syst., vol. 23, no. 6, pp. 1633–1651, Sep. 2021. doi: 10.1007/s40815-021-01058-1
    [80]
    O. Castillo, J. R. Castro, and P. Melin, “Interval type-3 fuzzy control for automated tuning of image quality in televisions,” Axioms, vol. 11, no. 6, p. 276, Jun. 2022. doi: 10.3390/axioms11060276
    [81]
    H. Bie, P. Li, F. Chen, and E. Ghaderpour, “An observer-based type-3 fuzzy control for non-holonomic wheeled robots,” Symmetry, vol. 15, no. 7, p. 1354, Jul. 2023. doi: 10.3390/sym15071354
    [82]
    M.-W. Tian, A. Mohammadzadeh, J. Tavoosi, S. Mobayen, J. H. Asad, O. Castillo, and A. R. Várkonyi-Kóczy, “A deep-learned type-3 fuzzy system and its application in modeling problems,” Acta Polytech. Hung., vol. 19, no. 2, pp. 151–172, Jan. 2022. doi: 10.12700/APH.19.2.2022.2.9
    [83]
    N. Nabipour, S. N. Qasem, and K. Jermsittiparsert, “Type-3 fuzzy voltage management in pv/hydrogen fuel cell/battery hybrid systems,” Int. J. Hyd. Energy, vol. 45, no. 56, pp. 32478–32492, Nov. 2020. doi: 10.1016/j.ijhydene.2020.08.261
    [84]
    M. R. Mashinchi, A. Selamat, and S. Ibrahim, “Evaluating extant uranium: Linguistic reasoning by fuzzy artificial neural networks,” in Proc. 14th Int. Conf., Naples, Italy, 2015, pp. 296–307.
    [85]
    M.-W. Tian, S.-R. Yan, J. Liu, K. A. Alattas, A. Mohammadzadeh, and M. T. Vu, “A new type-3 fuzzy logic approach for chaotic systems: Robust learning algorithm,” Mathematics, vol. 10, no. 15, p. 2594, Jul. 2022. doi: 10.3390/math10152594
    [86]
    O. Castillo, J. R. Castro, and P. Melin, “Forecasting the COVID-19 with interval type-3 fuzzy logic and the fractal dimension,” Int. J. Fuzzy Syst., vol. 25, no. 1, pp. 182–197, Feb. 2023. doi: 10.1007/s40815-022-01351-7
    [87]
    P. Melin and O. Castillo, “Interval type-3 fuzzy decision making in quality evaluation for speaker manufacturing,” in Hybrid Intelligent Systems Based on Extensions of Fuzzy Logic, Neural Networks and Metaheuristics, O. Castillo and P. Melin, Eds. Cham, Germany: Springer, 2023, pp. 489–498.
    [88]
    M. Gheisarnejad, A. Mohammadzadeh, H. Farsizadeh, and M.-H. Khooban, “Stabilization of 5G telecom converter-based deep type-3 fuzzy machine learning control for telecom applications,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 69, no. 2, pp. 544–548, Feb. 2022.
    [89]
    A. Mosavi, S. N. Qasem, M. Shokri, S. S. Band, and A. Mohammadzadeh, “Fractional-order fuzzy control approach for photovoltaic/battery systems under unknown dynamics, variable irradiation and temperature,” Electronics, vol. 9, no. 9, p. 1455, Sep. 2020. doi: 10.3390/electronics9091455
    [90]
    A. Tarafdar, P. Majumder, and U. K. Bera, “Prediction of air quality index in Kolkata city using an advanced learned interval type-3 fuzzy logic system,” in Proc. 2023 IEEE 8th Int. Conf. for Convergence in Technology, Lonavla, India, 2023, pp. 1–7.
    [91]
    A. Taghieh, A. Mohammadzadeh, C. Zhang, S. Rathinasamy, and S. Bekiros, “A novel adaptive interval type-3 neuro-fuzzy robust controller for nonlinear complex dynamical systems with inherent uncertainties,” Nonlinear Dyn., vol. 111, no. 1, pp. 411–425, Jan. 2023. doi: 10.1007/s11071-022-07867-9
    [92]
    S. Yan, A. A. Aly, B. F. Felemban, M. Gheisarnejad, M. Tian, M. H. Khooban, A. Mohammadzadeh, and S. Mobayen, “A new event-triggered type-3 fuzzy control system for multi-agent systems: Optimal economic efficient approach for actuator activating,” Electronics, vol. 10, no. 24, p. 3122, Dec. 2021. doi: 10.3390/electronics10243122
    [93]
    Y. Cao, A. Raise, A. Mohammadzadeh, S. Rathinasamy, S. S. Band, and A. Mosavi, “Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction,” Energy Rep., vol. 7, pp. 8115–8127, Nov. 2021. doi: 10.1016/j.egyr.2021.07.004
    [94]
    A. A. Aly, B. F. Felemban, A. Mohammadzadeh, O. Castillo, and A. Bartoszewicz, “Frequency regulation system: A deep learning identification, type-3 fuzzy control and LMI stability analysis,” Energies, vol. 14, no. 22, p. 7801, Nov. 2021. doi: 10.3390/en14227801
    [95]
    L. Amador-Angulo, O. Castillo, P. Melin, and J. R. Castro, “Interval type-3 fuzzy adaptation of the bee colony optimization algorithm for optimal fuzzy control of an autonomous mobile robot,” Micromachines, vol. 13, no. 9, p. 1490, Sep. 2022. doi: 10.3390/mi13091490
    [96]
    N. R. Haddaway, M. J. Page, C. C. Pritchard, and L. A. McGuinness, “PRISMA2020: An R package and shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and open synthesis,” Campbell Syst. Rev., vol. 18, no. 2, p. e1230, Jun. 2022. doi: 10.1002/cl2.1230
    [97]
    O. Castillo, J. R. Castro, and P. Melin, Interval Type-3 Fuzzy Systems: Theory and Design. Cham, Germany: Springer, 2022.
    [98]
    H. Small, “Visualizing science by citation mapping,” J. Am. Soc. Inf. Sci., vol. 50, no. 9, pp. 799–813, Jul. 1999. doi: 10.1002/(SICI)1097-4571(1999)50:9<799::AID-ASI9>3.0.CO;2-G
    [99]
    M. Gheisarnejad, A. Mohammadzadeh, and M.-H. Khooban, “Model predictive control based type-3 fuzzy estimator for voltage stabilization of DC power converters,” IEEE Trans. Ind. Electron., vol. 69, no. 12, pp. 13849–13858, Dec. 2022. doi: 10.1109/TIE.2021.3134052
    [100]
    C. Chen, “CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature,” J. Am. Soc. Inf. Sci. Technol., vol. 57, no. 3, pp. 359–377, Feb. 2006. doi: 10.1002/asi.20317
    [101]
    S. K. Sood, K. S. Rawat, and D. Kumar, “Analytical mapping of information and communication technology in emerging infectious diseases using CiteSpace,” Telematics Inform., vol. 69, p. 101796, Apr. 2022. doi: 10.1016/j.tele.2022.101796
    [102]
    S. Bai, F. Bao, and F. Zhao, “”Smart classroom” research hotspots and frontier analysis—Visual analysis based on CiteSpace,” in Proc. 2021 3rd Int. Conf. on Artificial Intelligence and Advanced Manufacture, Manchester, UK, 2021, pp. 1295–1299.

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(14)  / Tables(11)

    Article Metrics

    Article views (86) PDF downloads(41) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return