GrNFS: A Granular Neuro–Fuzzy System for Regression in Large Volume Data

Ahmed, M.M. and Isa, N.A.M. (2017). Knowledge base to fuzzy information granule: A review from the interpretability-accuracy perspective, Applied Soft Computing 54: 121–140.10.1016/j.asoc.2016.12.055 Search in Google Scholar

Alcalá, R., Alcalá-Fdez, J., Casillas, J., Cordón, O. and Herrera, F. (2006). Hybrid learning models to get the interpretability-accuracy trade-off in fuzzy modeling, Soft Computing 10(9): 717–734.10.1007/s00500-005-0002-1 Search in Google Scholar

Alonso, J.M. and Magdalena, L. (2011). Special issue on interpretable fuzzy systems, Information Sciences 181(20): 4331–4339.10.1016/j.ins.2011.07.001 Search in Google Scholar

Atanassov, K.T. (1986). Intuitionistic fuzzy sets, Fuzzy Sets and Systems 20(1): 87–96.10.1016/S0165-0114(86)80034-3 Search in Google Scholar

Bargiela, A. and Pedrycz, W. (2006). The roots of granular computing, 2006 IEEE International Conference on Granular Computing, Atlanta, USA, pp. 806–809. Search in Google Scholar

Bisi, C., Chiaselotti, G., Ciucci, D., Gentile, T. and Infusino, F.G. (2017). Micro and macro models of granular computing induced by the indiscernibility relation, Information Sciences 388–389: 247–273.10.1016/j.ins.2017.01.023 Search in Google Scholar

Botta, A., Lazzerini, B., Marcelloni, F. and Stefanescu, D.C. (2009). Context adaptation of fuzzy systems through a multi-objective evolutionary approach based on a novel interpretability index, Soft Computing 13(5): 437–449.10.1007/s00500-008-0360-6 Search in Google Scholar

Ciucci, D. (2016). Orthopairs and granular computing, Granular Computing 1: 159–170.10.1007/s41066-015-0013-y Search in Google Scholar

Cpałka, K., Łapa, K., Przybył, A. and Zalasiński, M. (2014). A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects, Neuro-computing 135: 203–217.10.1016/j.neucom.2013.12.031 Search in Google Scholar

Dunn, J.C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact, well separated clusters, Journal Cybernetics 3(3): 32–57.10.1080/01969727308546046 Search in Google Scholar

Evsukoff, A.G., Galichet, S., de Lima, B.S. and Ebecken, N.F. (2009). Design of interpretable fuzzy rule-based classifiers using spectral analysis with structure and parameters optimization, Fuzzy Sets and Systems 160(7): 857–881.10.1016/j.fss.2008.08.010 Search in Google Scholar

Gacto, M.J., Alcalá, R. and Herrera, F. (2011). Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures, Information Sciences 181(20): 4340–4360.10.1016/j.ins.2011.02.021 Search in Google Scholar

Herrera, L.J., Pomares, H., Rojas, I., Valenzuela, O. and Prieto, A. (2005). TASE, a Taylor series-based fuzzy system model that combines interpretability and accuracy, Fuzzy Sets and Systems 153(3): 403–427.10.1016/j.fss.2005.01.012 Search in Google Scholar

Hu, X., Pedrycz, W., Wu, G. and Wang, X. (2017). Data reconstruction with information granules: An augmented method of fuzzy clustering, Applied Soft Computing 55: 523–532.10.1016/j.asoc.2017.02.014 Search in Google Scholar

Juang, C.-F. and Chen, C.-Y. (2013). Data-driven interval type-2 neural fuzzy system with high learning accuracy and improved model interpretability, IEEE Transactions on Cybernetics 43(6): 1781–1795.10.1109/TSMCB.2012.2230253 Search in Google Scholar

Juang, C.-F. and Lin, C.-T. (1998). An online self-constructing neural fuzzy inference network and its applications, IEEE Transactions on Fuzzy Systems 6(1): 12–32.10.1109/91.660805 Search in Google Scholar

Juang, C.F. and Tsao, Y.W. (2008). A type-2 self-organizing neural fuzzy system and its FPGA implementation, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38(6): 1537–1548.10.1109/TSMCB.2008.927713 Search in Google Scholar

Keet, C.M. (2008). A Formal Theory of Granularity, PhD thesis, Free University of Bozen-Bolzano, Bolzano. Search in Google Scholar

Leski, J. (2008). Neuro-Fuzzy Systems, Scientific and Engineering Publishers WNT, Warsaw, (in Polish). Search in Google Scholar

Lorenz, E.N. (1963). Deterministic nonperiodic flow, Journal of the Atmospheric Sciences 20(2): 130–141.10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2 Search in Google Scholar

Mackey, M.C. and Glass, L. (1977). Oscillation and chaos in physiological control systems, Science 197(4300): 287–289.10.1126/science.267326 Search in Google Scholar

Mendel, J.M. (2004). Computing derivatives in interval type-2 fuzzy logic systems, IEEE Transactions on Fuzzy Systems 12(1): 84–98.10.1109/TFUZZ.2003.822681 Search in Google Scholar

Pawlak, Z. (1996). Rough sets, rough relations and rough functions, Fundamenta Informaticae 27(2): 103–108.10.3233/FI-1996-272301 Search in Google Scholar

Pedrycz, A., Hirota, K., Pedrycz, W. and Dong, F. (2012). Granular representation and granular computing with fuzzy sets, Fuzzy Sets and Systems 203: 17–32.10.1016/j.fss.2012.03.009 Search in Google Scholar

Pedrycz, W. (1998). Shadowed sets: Representing and processing fuzzy sets, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 28(1): 103–109.10.1109/3477.65858418255928 Search in Google Scholar

Pedrycz, W. (2013). Granular Computing: Analysis and Design of Intelligent Systems, CRC Press, Boca Raton.10.1201/b14862 Search in Google Scholar

Pedrycz, W. and Gomide, F. (2007). Fuzzy Systems Engineering: Toward Human-Centric Computing, John Wiley, Hoboken.10.1002/9780470168967 Search in Google Scholar

Pedrycz, W., Hmouz, R.A., Balamash, A.S. and Morfeq, A. (2015a). Hierarchical granular clustering: An emergence of information granules of higher type and higher order, IEEE Transactions on Fuzzy Systems 23(6): 2270–2283.10.1109/TFUZZ.2015.2417896 Search in Google Scholar

Pedrycz, W. and Homenda, W. (2013). Building the fundamentals of granular computing: A principle of justifiable granularity, Applied Soft Computing 13(10): 4209–4218.10.1016/j.asoc.2013.06.017 Search in Google Scholar

Pedrycz, W., Succi, G., Sillitti, A. and Iljazi, J. (2015b). Data description: A general framework of information granules, Knowledge-Based Systems 80: 98–108.10.1016/j.knosys.2014.12.030 Search in Google Scholar

Qian, Y.H., Liang, J.Y., Yao, Y.Y. and Dang, C.Y. (2010). MGRS: A multi-granulation rough set, Information Sciences 180(6): 949–970.10.1016/j.ins.2009.11.023 Search in Google Scholar

Qian, Y., Liang, J., Wu, W.-Z. and Dang, C. (2011). Information granularity in fuzzy binary GrC model, IEEE Transactions on Fuzzy Systems 19(2): 253–264.10.1109/TFUZZ.2010.2095461 Search in Google Scholar

Reyes-Galaviz, O.F. and Pedrycz, W. (2015). Granular fuzzy models: Analysis, design, and evaluation, International Journal of Approximate Reasoning 64: 1–19.10.1016/j.ijar.2015.06.005 Search in Google Scholar

Rössler, O.E. (1976). An equation for continuous chaos, Physics Letters A 57(5): 397–398.10.1016/0375-9601(76)90101-8 Search in Google Scholar

Salehi, S., Selamat, A. and Fujita, H. (2015). Systematic mapping study on granular computing, Knowledge-Based Systems 80: 78–97.10.1016/j.knosys.2015.02.018 Search in Google Scholar

Shifei, D., Li, X., Hong, Z. and Liwen, Z. (2010). Research and progress of cluster algorithms based on granular computing, International Journal of Digital Content Technology and Its Applications 4(5): 96–104.10.4156/jdcta.vol4.issue5.11 Search in Google Scholar

Siminski, K. (2014). Neuro-fuzzy system with weighted attributes, Soft Computing 18(2): 285–297.10.1007/s00500-013-1057-z Search in Google Scholar

Siminski, K. (2015). Rough subspace neuro-fuzzy system, Fuzzy Sets and Systems 269: 30–46.10.1016/j.fss.2014.07.003 Search in Google Scholar

Siminski, K. (2017). Interval type-2 neuro-fuzzy system with implication-based inference mechanism, Expert Systems with Applications 79C: 140–152.10.1016/j.eswa.2017.02.046 Search in Google Scholar

Siminski, K. (2019). NFL—free library for fuzzy and neuro-fuzzy systems, in S. Kozielski et al. (Eds), Beyond Databases, Architectures and Structures. Paving the Road to Smart Data Processing and Analysis, Springer International Publishing, Cham, pp. 139–150.10.1007/978-3-030-19093-4_11 Search in Google Scholar

Siminski, K. (2020). GrFCM—Granular clustering of granular data, in A. Gruca et al. (Eds), Man–Machine Interactions 6, Springer, Cham, pp. 111–121.10.1007/978-3-030-31964-9_11 Search in Google Scholar

Siminski, K. (2021). An outlier-robust neuro-fuzzy system for classification and regression, International Journal of Applied Mathematics and Computer Science 31(2): 303–319, DOI: 10.34768/amcs-2021-0021. Search in Google Scholar

Skowron, A., Jankowski, A. and Dutta, S. (2016). Interactive granular computing, Granular Computing 1: 95–113.10.1007/s41066-015-0002-1 Search in Google Scholar

Sugeno, M. and Kang, G.T. (1988). Structure identification of fuzzy model, Fuzzy Sets ans Systems 28(1): 15–33.10.1016/0165-0114(88)90113-3 Search in Google Scholar

Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its application to modeling and control, IEEE Transactions on Systems, Man and Cybernetics 15(1): 116–132.10.1109/TSMC.1985.6313399 Search in Google Scholar

Wang, D., Pedrycz, W. and Li, Z. (2019). Granular data aggregation: An adaptive principle of the justifiable granularity approach, IEEE Transactions on Cybernetics 49(2): 1–10.10.1109/TCYB.2017.2774831 Search in Google Scholar

Yang, X., Li, T., Liu, D. and Fujita, H. (2019). A temporal-spatial composite sequential approach of three-way granular computing, Information Sciences 486: 171–189.10.1016/j.ins.2019.02.048 Search in Google Scholar

Yao, J.T., Vasilakos, A.V. and Pedrycz, W. (2013). Granular computing: Perspectives and challenges, IEEE Transactions on Cybernetics 43(6): 1977–1989.10.1109/TSMCC.2012.2236648 Search in Google Scholar

Yao, Y. (2007). The art of granular computing, in M. Kryszkiewicz et al. (Eds), Rough Sets and Intelligent Systems Paradigms, Springer, Berlin/Heidelberg, pp. 101–112.10.1007/978-3-540-73451-2_12 Search in Google Scholar

Yao, Y. (2008). Granular computing: Past, present and future, 2008 IEEE International Conference on Granular Computing, GrC 2008, Hangzhou, China, pp. 80–85. Search in Google Scholar

Yao, Y. (2018). Three-way decision and granular computing, International Journal of Approximate Reasoning 103: 107–123.10.1016/j.ijar.2018.09.005 Search in Google Scholar

Yao, Y. (2020). Three-way granular computing, rough sets, and formal concept analysis, International Journal of Approximate Reasoning 116: 106–125.10.1016/j.ijar.2019.11.002 Search in Google Scholar

Yao, Y. and Zhong, N. (2007). Granular computing, in B.W. Wah (Ed.), Wiley Encyclopedia of Computer Science and Engineering, Wiley, Hoboken.10.1002/9780470050118.ecse468 Search in Google Scholar

Yen, J., Wang, L. and Gillespie, C. W. (1998). Improving the interpretability of TSK fuzzy models by combining global learning and local learning, IEEE Transactions on Fuzzy Systems 6(4): 530–537.10.1109/91.728447 Search in Google Scholar

Zadeh, L.A. (1965). Fuzzy sets, Information and Control 8: 338–353.10.1016/S0019-9958(65)90241-X Search in Google Scholar

Zadeh, L.A. (1979). Fuzzy sets and information granularity, in N. Gupta et al. (Eds), Advances in Fuzzy Set Theory and Applications, North-Holland Publishing, Amsterdam, pp. 3–18. Search in Google Scholar

Zadeh, L.A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic, Fuzzy Sets and Systems 90(2): 111–127.10.1016/S0165-0114(97)00077-8 Search in Google Scholar

Zadeh, L.A. (2002). From computing with numbers to computing with words—From manipulation of measurements to manipulation of perceptions, International Journal of Applied Mathematics and Computer Science 12(3): 307–324.10.1063/1.1388678 Search in Google Scholar