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Data-Driven Evolving Fuzzy Models - Algorithms and Applications

Tuesday 12 September 2006, 1400-1500
C60 InfoLab21

By Dr Edwin Lughofer, Johan Kepler University, Linz, Austria

In nowadays industrial systems there are quite a lot requirements for an incremental modelling of system behaviours from data. These requirements arise due to a fast tracking of dependencies between system variables with online recorded measurements or due to huge data-bases which cannot be loaded into virtual memory at once. Improving the process security by extending already available models to new operating conditions or by adjusting them on the basis of some feedback from operators is an important issue as well.

In this talk algorithms for a data-driven incremental learning of fuzzy models are demonstrated. The main focus is given to fuzzy basis function networks (as a specific type of Takagi-Sugeno fuzzy models), but some aspects how to extend the approach to other types of fuzzy models are illuminated as well. A modified version of vector quantization is exploited for rule evolution and an incremental learning of the rules' premise parts. The modifications include the generation of new clusters due to the nature, distribution and quality of new data, point-wise update of already existing clusters and an alternative distance strategy for selecting the most adjacent clusters for each new incoming sample. The premise part learning is connected in a stable manner with a recursive learning of rule consequent functions possessing linear parameters. Stability can be achieved by preventing the 'unlearning' effect in case of steady states and by ensuring that a suboptimal solution of the parameters can be achieved which is close to the optimal one in the least squares sense (i.e. with respect to least squares as underlying optimization function). Some aspects about maintaining the interpretability of the trained fuzzy models round off the algorithmic part of the talk. The second part is focussed on applications of data-driven evolving fuzzy models in industrial systems. These applications include online identification for high-dimensional static and dynamic data, online fault detection, refinement of expert-knowledge based fuzzy systems with data and model-based (inverse and predictive) control. Some evaluation results for online identification and fault detection are mentioned.