Abstract
A projection based method for sparse fuzzy system generation is proposed. Given a set of training data, clustering is first performed on the output space. Data points from each output cluster are projected back to each input dimension forming one-dimensional clusters. The clusters from different dimension are then merged to form fuzzy rules. Experiments have confirmed the effectiveness of the proposed technique.