Granular modelling through regression analysis
نویسنده
چکیده
Granular representation of experimental data D is defined as a transformation A G(Y) where A are information granules in the space of input data D and G(Y) is a family of information granules in the output space. In this paper, we propose an alternative to the granular representation method based on the use of possibility and necessity measure and the solution of fuzzy relational equations. The proposed method is an iterative multiple regression with fuzzy independent and dependent variables. The regression problem is posed as a gradient descent optimisation which provides both computational efficiency and generality.
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