Parameter estimation for Choquet fuzzy integral based on Takagi-Sugeno fuzzy model

Currently, both Choquet fuzzy integral and Takagi-Sugeno fuzzy models are popular synthetic evaluation and fuzzy modeling tools. In this paper, we prove that Choquet fuzzy integral is a special version of Takagi-Suguno fuzzy model in the sense of structure, thus the learning algorithm of the latter is used to develop a parameter estimation procedure for the former. The parameter estimation procedure actually is performed in each ordinal subspace of input space, in which all input data have unique ordering of components. The proposed approach in this paper has been proven to possess better performance than the existing ones by not only theoretical analysis but also experiments.