Data-Driven Incremental Learning of Takagi-Sugeno Fuzzy Models
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Nowadays data-driven models become more and more an essential part in industrial systems for application tasks such as
system identification and analysis, prediction, control, fault detection or simply simulation.
Data driven models are mathematical
models which are completely identified from data, which can be available in form of offline data sets,
most commonly stored in data matrices, or
in form of online measurements. Data-driven models possess the nice property that they can be built
up generically in the sense that no underlying
physical, chemical etc. laws about the system variables have to be known.
%which are recorded with a certain frequency within an online process.
Whenever measurements are recorded online with a certain frequency,
usually the models should be kept up-to-date from time to time, especially when tracking
highly time-variant system behaviors for online identification tasks, which requires an adaptation of some model
parameters in form of incremental learning
steps, as a complete rebuilding from time to time with all recorded measurements would yield a too high computational
effort for a complete online training.
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Begutachter: Erich Peter Klement, Eyke Hüllermeier