Evolving Linguistic Fuzzy Models from Data Streams

Abstract

This work outlines a new approach for online learning from imprecise data, namely, fuzzy set based evolving modeling (FBeM) approach. FBeM is an adaptive modeling framework that uses fuzzy granular objects to enclose uncertainty in the data. The FBeM algorithm is data flow driven and supports learning on an instance-per-instance recursive basis by developing and refining fuzzy models on-demand. Structurally, FBeM models combine Mamdani and functional fuzzy systems to output granular and singular approximations of nonstationary functions. In general, approximand functions can be time series, decision boundaries between classes, and control and regression functions. Linguistic description of the behavior of the system over time is provided by information granules and associated rules. An application example on a reactive control problem, underlining the complemen-tarity of Mamdani and functional parts of the model, illustrates the usefulness of the approach. More specifically, the problem concerns sensor-based robot navigation and localization. In addition to precise singular output values, granular output values provide effective robust obstacle avoidance navigation.