Learning fuzzy systems: an ojective function-approach

One of the most important aspects of fuzzy systems is that they are
easily understandable and interpretable. This property, however, does not
come for free but poses some essential constraints on the parameters of a
fuzzy system (like the linguistic terms), which are sometimes overlooked when
learning fuzzy system automatically from data. In this paper, an objective
function-based approach to learn fuzzy systems is developed, taking these
constraints explicitly into account. Starting from fuzzy c-means clustering,
several modifications of the basic algorithm are proposed, affecting the shape
of the membership functions, the partition of individual variables and the
coupling of input space partitioning and local function approximation.