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Automatic input space partitioning for hierarchical fuzzy systems

The paper is based on a new learning algorithm for Hierarchical Fuzzy Associative Memories (HIFAM) recently proposed by the author. It has been demonstrated that the proposed method is suited for classification problems as well as for regression tasks and that it compares well to existing machine learning techniques. The paper investigates an extension to the HIFAM method that allows the simultaneous creation of rules and fuzzy sets. Several results of the approach on commonly used benchmark data sets are given and compared to the results of the original algorithm.