March 28, 2017

Download Accuracy Improvements in Linguistic Fuzzy Modeling by Jorge Casillas, O. Cordón, Francisco Herrera Triguero, Luis PDF

By Jorge Casillas, O. Cordón, Francisco Herrera Triguero, Luis Magdalena

Fuzzy modeling frequently comes with contradictory requisites: interpretability, that's the aptitude to specific the true approach habit in a understandable method, and accuracy, that is the potential to faithfully characterize the true approach. during this framework, essentially the most very important components is linguistic fuzzy modeling, the place the legibility of the got version is the most target. This activity is mostly constructed through linguistic (Mamdani) fuzzy rule-based platforms. An lively study sector is orientated in the direction of using new recommendations and constructions to increase the classical, inflexible linguistic fuzzy modeling with the most objective of accelerating its precision measure. generally, this accuracy development has been conducted with out contemplating the corresponding interpretability loss. at the moment, new traits were proposed attempting to guard the linguistic fuzzy version description energy through the optimization strategy. Written by way of prime specialists within the box, this quantity collects a few consultant researcher that pursue this technique.

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E. Bonabeau, M. Dorigo, and G. Theraulaz. Swarm intelligence. From natural to artificial systems. Oxford University Press, Oxford, UK, 1999. 2. B. F. Harti, and C. Strauss. A new rank based version of the ant system: a computational study. Central European Journal for Operations Research and Economics, 7(1):25-38, 1999. 3. J. Casillas, O. Cordon, and F. Herrera. COR: A methodology to improve ad hoc data-driven linguistic rule learning methods by inducing cooperation among rules. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybemetics.

EN }, with el = (xL . , x~, yl), l E {I, ... , N}, N being the data set size, and n being the number of input variables - representing the behavior of the prob/em being so/ved. • A fuzzy partition of the variab/e spaces. In our case, uniformly distributed fuzzy sets are regarded. Let Ai be the set of linguistic terms of the i-th input variable, with i E {I, ... , n}, and 8 be the set of linguistic terms of the output variable, with lAii (181) being the number of labels of the i-th input (output) variable.

Therefore, instead of selecting the consequent with the highest performance in each fuzzy input subspace as other methods usually do, COR considers the possibility of using another consequent, different from the best one, when it allows the fuzzy model to be more accurate thanks to having a rule set with best cooperation. The obtained experimental results lead us to think that the simple learning procedure performed by the COR methodology obtains linguistic fuzzy models with an excellent interpretability and good accuracy.

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