Abstract
In this paper, we offer a new design methodology of type-2 fuzzy models whose intent is to effectively exploit the uncertainty of non-numeric membership functions. A new performance index, which guides the development of the fuzzy model, is used to navigate the construction of the fuzzy model. The underlying idea is that an optimal granularity allocation throughout the membership functions used in the fuzzy model leads to the best design. In contrast to the commonly utilized criterion where one strives for the highest accuracy of the model, the proposed index is formed in such a way so that the type-2 fuzzy model produced intervals, which "cover" the experimental data and at the same time are made as narrow (viz. specific) as possible. Genetic algorithm is proposed to automate the design process and further improve the results by carefully exploiting the search space. Experimental results show the efficiency of the proposed design methodology. (C) 2011 Elsevier B.V. All rights reserved.