By Jorge Casillas, O. Cordón, Francisco Herrera Triguero, Luis Magdalena
Fuzzy modeling often comes with contradictory standards: interpretability, that is the aptitude to precise the true procedure habit in a understandable method, and accuracy, that is the aptitude to faithfully symbolize the true approach. during this framework, the most vital components is linguistic fuzzy modeling, the place the legibility of the got version is the most goal. This activity is mostly built via linguistic (Mamdani) fuzzy rule-based platforms. An lively study region is orientated in the direction of using new ideas and constructions to increase the classical, inflexible linguistic fuzzy modeling with the most target of accelerating its precision measure. typically, this accuracy development has been performed with no contemplating the corresponding interpretability loss. at the moment, new developments were proposed attempting to safeguard the linguistic fuzzy version description energy in the course of the optimization approach. Written through major specialists within the box, this quantity collects a few consultant researcher that pursue this method.
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Additional resources for Accuracy Improvements in Linguistic Fuzzy Modeling
IEEE Transactions on Fuzzy Systems, 8(2):212221,2000. 45. Y. Jin, W. von Seelen, and B. Sendhoff. On generat ing FC 3 fuzzy rule systems from data using evolution strategies. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 29(4):829-845,1999. 46. L. Karr. Genetic algorithms for fuzzy controllers. AI Expert, 6(2):26-33, 1991. 47. F. Klawonn. Fuzzy sets and vague environments. Fuzzy Sets and Systems, 66:207-221, 1994. 48. K. KrishnaKumar and A. Satyadas. GA-optimized fuzzy controller for spacecraft attitude control.
Espinosa and J. Vandewalle. Constructing fuzzy models with linguistic integrity from numerical data-AFRELI algorithm. IEEE Transactions on Fuzzy Systems, 8(5):591-600, 2000. 12. A. Gonzalez and R. perez. A study about the inclusion of linguistic hedges in a fuzzy rule learning algorithm. International Joumal of Uncertainty, Fuzziness and Knowledge-Based Systems, 7(3):257-266, 1999. 13. F. Herrera, M. L. Verdegay. A learning process for fuzzy control rules using genetic algorithms. Fuzzy Sets and Systems, 100: 143-158, 1998.
Their improvements mainly consist of using different transition and update rules, introduc ing new components, or adding a local search phase [2,9,24]. To apply ACO algorithms to a specific problem, the five steps shown in Fig. 5 have to be performed. The following sections describe these aspects particularized to the COR methodology. 1. Problem representation: Interpret the problem to be solved as a graph or a similar structure easily traveled by ants. 2. Heuristic information: Define the way of assigning a heuristic preference to each choice that the ant has to take in each step to generate the solution.
Accuracy Improvements in Linguistic Fuzzy Modeling by Jorge Casillas, O. Cordón, Francisco Herrera Triguero, Luis Magdalena