Voir la notice de l'article provenant de la source Library of Science
@article{IJAMCS_2021_31_3_a4, author = {Kud{\l}acik, Przemys{\l}aw}, title = {Uncertainty in the conjunctive approach to fuzzy inference}, journal = {International Journal of Applied Mathematics and Computer Science}, pages = {431--444}, publisher = {mathdoc}, volume = {31}, number = {3}, year = {2021}, language = {en}, url = {http://geodesic.mathdoc.fr/item/IJAMCS_2021_31_3_a4/} }
TY - JOUR AU - Kudłacik, Przemysław TI - Uncertainty in the conjunctive approach to fuzzy inference JO - International Journal of Applied Mathematics and Computer Science PY - 2021 SP - 431 EP - 444 VL - 31 IS - 3 PB - mathdoc UR - http://geodesic.mathdoc.fr/item/IJAMCS_2021_31_3_a4/ LA - en ID - IJAMCS_2021_31_3_a4 ER -
Kudłacik, Przemysław. Uncertainty in the conjunctive approach to fuzzy inference. International Journal of Applied Mathematics and Computer Science, Tome 31 (2021) no. 3, pp. 431-444. http://geodesic.mathdoc.fr/item/IJAMCS_2021_31_3_a4/
[1] [1] Azzini, A., Marrara, S., Sassi, R. and Scotti, F. (2008). A fuzzy approach to multimodal biometric continuous authentication, Fuzzy Optimization and Decision Making 7(243): 243–256.
[2] [2] Baldwin, J. (1979a). Advances in Fuzzy Set Theory and Applications, North-Holland, Amsterdam, pp. 93–115.
[3] [3] Baldwin, J. (1979b). Fuzzy logic and fuzzy reasoning, International Journal of Man-Machine Studies 11(4): 465–480.
[4] [4] Baldwin, J. (1979c). A new approach to approximate reasoning using a fuzzy logic, Fuzzy Sets and Systems 2(4): 309–325.
[5] [5] Bellman, R. and Zadeh, L. (1977). Modern Uses of Multiple-Valued Logic. Episteme, Springer, Dordrecht, pp. 103–165.
[6] [6] Cordon, O., Herrera, F. and Peregrin, A. (1997). Applicability of the fuzzy operators in the design of fuzzy logic controllers, Fuzzy Sets and Systems 86(1): 15–41.
[7] [7] Czabanski, R., Jezewski, M. and Leski, J. (2017). Introduction to Fuzzy Systems, Springer, Cham, pp. 23–43.
[8] [8] Czogała, E. and Kowalczyk, R. (1996). Investigation of selected fuzzy operations and implications for engineering, IEEE 5th International Conference Fuzzy Systems, New Orleans, USA, pp. 879–885.
[9] [9] Czogała, E. and Łęski, J. (2000). Fuzzy and Neuro-Fuzzy Intelligent Systems, Physica, Springer-Verlag, Heidelberg.
[10] [10] Czogała, E. and Łęski, J. (2001). On equivalence of approximate reasoning results using different interpretations of if-then rules, Fuzzy Sets and Systems 117(2): 279–296.
[11] [11] Dubois, D. and Prade, H. (1999). Fuzzy sets in approximate reasoning. Part 1: Inference with possibility distribution, Fuzzy Sets and Systems 100(Supp. 1): 73–132.
[12] [12] Dubois, D. and Prade, H. (1996). What are fuzzy rules and how to use them, Fuzzy Sets and Systems 84(2): 169–185.
[13] [13] Grzegorzewski, P., Hryniewicz, O. and Romaniuk, M. (2020). Flexible resampling for fuzzy data, International Journal of Applied Mathematics and Computer Science 30(2): 281–297, DOI: 10.34768/amcs-2020-0022.
[14] [14] Ho, C., Li, J. and Gwak, S. (2010). Research of a new fuzzy reasoning method by moving of fuzzy membership functions, 2010 International Symposium on Intelligence Information Processing and Trusted Computing, Huanggang, China, pp. 297–300.
[15] [15] Izquierdo, S.S. and Izquierdo, L.R. (2018). Mamdani fuzzy systems for modelling and simulation: A critical assessment, Journal of Artificial Societies and Social Simulation 21(3): 2.
[16] [16] Klir, G.J., Clair, U.S. and Yuan, B. (1997). Fuzzy Set Theory: Foundations and Applications, Prentice Hall, Upper Saddle River.
[17] [17] Kudłacik, P. (2010). Advantages of an approximate reasoning based on a fuzzy truth value, Medical Informatics Technologies 16: 125–132.
[18] [18] Kudłacik, P. (2012). Performance evaluation of Baldwin’s fuzzy reasoning for large knowledge bases, Medical Informatics Technologies 20: 29–38.
[19] [19] Kudłacik, P. (2013). An analysis of using triangular truth function in fuzzy reasoning based on a fuzzy truth value, Medical Informatics Technologies 22: 103–110.
[20] [20] Kudłacik, P. and Łęski, J. (2021). Practical aspects of equivalence of Baldwin’s and Zadeh’s fuzzy inference, Journal of Intelligent Fuzzy Systems 40(3): 4617–4636.
[21] [21] Mamdani, E. and Assilan, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies 20(2): 1–13.
[22] [22] Mazandarani, M. and Xiu, L. (2020). Fractional fuzzy inference system: The new generation of fuzzy inference systems, IEEE Access 8: 126066–126082.
[23] [23] Mizumoto, M. and Zimmermann, H.-J. (1982). Comparison of fuzzy reasoning methods, Fuzzy Sets and Systems 8(3): 253–283.
[24] [24] Piegat, A. and Dobryakova, L. (2020). A decomposition approach to type 2 interval arithmetic, International Journal of Applied Mathematics and Computer Science 30(1): 185–201, DOI: 10.34768/amcs-2020-0015.
[25] [25] Rutkowski, L. (2008). Computational Intelligence, Methods and Techniques, Springer, Berlin/Heidelberg.
[26] [26] Tong, R.M. and Festathiou, J. (1982). A critical assessment of truth function modification and its use in approximate reasoning, Fuzzy Sets and Systems 7(1): 103–108.
[27] [27] Ughetto, L., Dubois, D. and Prade, H. (1999). Implicative and conjunctive fuzzy rules—A tool for reasoning from knowledge and examples, 16th National Conference on Artificial Intelligence/11th Annual Conference on Innovative Applications of Artificial Intelligence, Orlando, USA, pp. 214–219.
[28] [28] Yagger, R. (1996). On the interpretation of fuzzy if-then rules, Applied Intelligence 6(2): 141–151.
[29] [29] Zadeh, L. (1973). Outline of a new approach to the analysis of complex systems and decision processes, IEEE Transactions on Systems, Man and Cybernetics 3(1): 28–44.
[30] [30] Zadeh, L. (1975). Fuzzy logic and approximate reasoning, Syntheses 30(3): 407–428.
[31] [31] Zimmermann, H.-J. (1985). Fuzzy Set Theory and Its Applications, Springer, Dordrecht.