By Jorge Casillas, O. Cordón, Francisco Herrera Triguero, Luis Magdalena
Fuzzy modeling often comes with contradictory requisites: interpretability, that's the aptitude to specific the true process habit in a understandable approach, and accuracy, that is the potential to faithfully signify the genuine approach. during this framework, essentially the most vital parts is linguistic fuzzy modeling, the place the legibility of the received version is the most aim. This job is generally constructed through linguistic (Mamdani) fuzzy rule-based platforms. An energetic study region is orientated in the direction of using new options and constructions to increase the classical, inflexible linguistic fuzzy modeling with the most objective of accelerating its precision measure. regularly, this accuracy development has been conducted with no contemplating the corresponding interpretability loss. at present, new tendencies were proposed attempting to guard the linguistic fuzzy version description energy through the optimization approach. Written via prime specialists within the box, this quantity collects a few consultant researcher that pursue this technique.
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Extra resources for Accuracy Improvements in Linguistic Fuzzy Modeling
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S method, the best results were obtained with o: = 5 in both problems, which are the results shown in the table. In the Thrift's method, a populat ion size of 61 41 Table 2. 2 as mutation probability per chromosome were used. 1 as creep probability were used. 2 respectively for each problem, LSi = 10, and LSn = 32 and LSn = 20 respectively for each problem. With respect to the heuristic information considered, the H3 and Hl functions (see Sect. 3) was used for the electrical line length and the electrical maintenance costs problems, respectively.
The ant system applied to the quadratic assignment problem. IEEE Transactions on Knowledge and Data Engineering, 11(5):769-778, 1999. D. Nauck and R. Kruse. Neuro-fuzzy systems for function approximaton. Fuzzy Sets and Systems, 101(2):261-271, 1999. K. Nozaki, H. Ishibuchi, and H. Tanaka. A simple but powerful heuristic method for generating fuzzy rules from numerical data. Fuzzy Sets and Systems, 86(3):251-270, 1997. R. Pal and K. Pal. Handling of inconsistent rules with an extended model of fuzzy reasoning.