Abstract
After a short introduction about type-2 FS and basics of Mamdani type fuzzy approximate, a possible influence of the fuzziness of fuzzy sets involved in approximate reasoning model is given. In the reasoning process the observed rule in the rule base system is weighted by the measure of fuzziness at the highest point of the common area of the rule premise and system input. The basic operators in the fuzzy approximate reasoning system are the distance-based (DB) operators.
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References
Mendel, J.M.: Type-2 Fuzzy Sets: Some Questions and Answers. IEEE Neural Network Society (August 2003)
Mendel, J.M., John, R.I.B.: Type-2 Fuzzy Sets Made Simple. IEEE Transactions on Fuzzy Systems 10(2), 117–127 (2002)
Zadeh, L.A.: The Concept of a Linguistic Variable and its Application to Approximate Reasoning – 1. Information Sciences 8, 199–249 (1975)
Ozen, T., Garibaldi, J.M.: Investigating Adaptation in Type-2 Fuzzy Logic Systems, Applied to Umbilical Acid-Base Assessment. In: Proc. the 2003 European Symposium on Intelligent Technologies (EUNITE 2003), Oulu, Finland, July 2003, pp. 289–294 (2003)
Sepulveda, R., Castillo, O., Melin, P., Rodriguez-Diaz, A., Montiel, O.: Experimental Study of Intelligent Controllers Under Uncertainty Using Type-1 and Type-2 Fuzzy Logic. Information Sciences 177, 2023–2048 (2007)
Wu, D., Tan, W.W.: Genetic Learning and Performance Evaluation of Type-2 Fuzzy Logic Controllers. Submitted to IEEE Trans. on Systems, Man, and Cybernetics—Part B: Cybernetics (2005)
Castro, J., Castillo, O., Mellin, P.: An Interval Type-2 Fuzzy Logic Toolbox for Control Applications. In: Proc. IEEE FUZZ Conference, London, UK, July 2007, pp. 61–66 (2007)
Thiele, H.: On Approximate Reasoning With Type-2 Fuzzy Sets. In: Proc. IPMU Conference, Annecy, France, July 1-5, 2002, pp. 355–362 (2002)
Wang, T., Chen, Y., Tong, S.: Fuzzy Reasoning Models and Algorithms on Type-2 Fuzzy Sets. International Journal of Innovative Computing, Information and Control ICIC International 4(10), 2451–2460 (2008)
Yager, R.R., Rybalov, A.: Uninorm Aggregation Operators. Fuzzy Sets and Systems 80, 111–120 (1996)
Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms. Kluwer Academic Publishers, Dordrecht (2000)
Yager, R.R.: Uninorms in Fuzzy System Modeling. Fuzzy Sets and Systems 122, 167–175 (2001)
Rudas, I.J.: Evolutionary Operators: New Parametric Type Operator Families. Fuzzy Sets and Systems 23, 149–166 (1999)
Takacs, M.: Approximate Reasoning in Fuzzy Systems Based On Pseudo-Analysis, Phd Thesis, Univ. of Novi Sad (2004)
Takacs, M., Pozna, C.: Quick Comparison of the Efficiency of Fuzzy Operatios Used in FLC. In: Proc. of SISY 2006, pp. 73–80 (2006)
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Takács, M. (2009). Fuzziness of Rule Outputs by the DB Operators-Based Control Problems. In: Rudas, I.J., Fodor, J., Kacprzyk, J. (eds) Towards Intelligent Engineering and Information Technology. Studies in Computational Intelligence, vol 243. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03737-5_47
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DOI: https://doi.org/10.1007/978-3-642-03737-5_47
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