A Hierarchical Fuzzy System with Automatical Rule Extraction

In this paper, we propose a hierarchical fuzzy system for high-dimensional data. We introduce a locally weighted scheme to the extraction of Takagi-Sugeno type rules. We apply the sequential least-squares method to estimate the linear model. A hierarchical clustering takes place in the product space of systems inputs and outputs, and each path from the root to a leaf corresponds to a fuzzy IF-THEN rule. Only a subset of the rules is considered, based on the locality of the input query data. At each hierarchy, a discriminating subspace is derived from the high-dimensional input space for a good generalization capability. Both a synthetic data set and a real-world robot collision avoidance problem are considered to illustrate how the algorithm works and the applicability of it