Data Driven Evolving Fuzzy Models - Algorithms and Advanced Aspects for Interpretability and Process Safety Enhancements
Sprache des Vortragstitels:
In nowadays industrial systems there are quite a lot requirements for an incremental modelling of system behaviors 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.