By Jorge Casillas, O. Cordón, Francisco Herrera Triguero, Luis Magdalena
Fuzzy modeling has turn into essentially the most effective and profitable result of fuzzy common sense. between others, it's been utilized to wisdom discovery, automated class, long term prediction, or clinical and engineering research. The examine constructed within the subject over the past 20 years has been commonly curious about exploiting the bushy version flexibility to procure the top accuracy. This method frequently units apart the interpretability of the got versions. besides the fact that, we must always keep in mind the preliminary philosophy of fuzzy units concept directed to serve the bridge among the human knowing and the laptop processing. during this problem, the power of fuzzy versions to specific the habit of the true procedure in a understandable demeanour acquires an excellent significance. This publication collects the works of a bunch of specialists within the box that suggest the interpretability advancements as a mechanism to procure good balanced fuzzy models.
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Extra info for Interpretability Issues in Fuzzy Modeling
Individual translation gave more (sometimes many more) rules. This is so because the GA tried to produce good translations locally. The pressure on the search exerted by the number of rules objective on individual strategy was not so high as the pressure by the same objective in carrying out group or global translations, where more data points were involved. It may be the case that, when using group or global strategies, several approximative rules can be covered by just one descriptive rule. This may produce potential savings in the number of rules generated.
When J. Casillas et al. ), Interpretability Issues in Fuzzy Modeling © Springer-Verlag Berlin Heidelberg 2003 25 employed unrestrictedly for fuzzy modelling, they tend to create fuzzy sets that fit the data extremely well but that usually lack features which are considered important to make it easy for human users to interpret the resulting model and its reasoning . Opposing this stands the descriptive approach (or linguistic fuzzy modelling). From this approach point of view, semantics are as important as accuracy.
S. Guillaume. Designing fuzzy inference systems from data: an interpretabilityoriented review. IEEE Transactions on Fuzzy Systems, 9(3):426-443, 2001. 28. M. Hangos, editor. Special issue on grey box modelling, volume 9(6) of International Journal of Adaptive Control and Signal Processing. John Wiley & Sons, New York, NY, USA, 1995. 29. F. Herrera, M. L. Verdegay. A learning process for fuzzy control rules using genetic algorithms. Fuzzy Sets and Systems, 100:143-158, 1998. 30. -P. -B. Chen. Finding relevant attributes and membership functions.
Interpretability Issues in Fuzzy Modeling by Jorge Casillas, O. Cordón, Francisco Herrera Triguero, Luis Magdalena