Improving the Interpretability of Data-Driven Evolving Fuzzy-Systems
Edwin David Lughofer, Eyke Hüllermeier, Erich Peter Klement · 2005
This paper develops methods for reducing the complexity and, thereby, improving the linguistic interpretability of Takagi-Sugeno fuzzy systems that are learned online in a data-driven, incremental way. In order to ensure the transparency of the evolving fuzzy system at any time, complexity reduction must be performed in an online mode as well. Our methods are evaluated on high-dimensional data coming from an industrial measuring process.