FLEXFIS: A Variant for Incremental Learning of Takagi-Sugeno Fuzzy Systems
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In this paper a new algorithm for the incremental learning of specific data-driven models, namely so-called Takagi-Sugeno fuzzy systems, is introduced. The new open-loop learning approach includes not only adaptation of linear parameters in fuzzy systems appearing in the rule consequents, but also sample mode adaptation of premise parameters appearing in the membership functions (i.e. fuzzy sets) together with a rule learning strategy. In this sense the proposed method is applicable for fast model training tasks in various industrial processes, whenever there is a demand of online system identification in order to apply models representing nonlinear system behaviors to system monitoring, online fault detection or open-loop control. An evaluation of the incremental learning algorithm is included at the end of the paper, where a comparison between conventional closed-loop modelling methods for fuzzy systems and the incremental learning method (also called adaptation in open-loop) demonstrated in this paper is made with respect to model qualities and computation time. This evaluation will be based on high dimensional data coming from an industrial measuring process as well as from a known source in the internet, which should underline the usage of the new method for fast online identification tasks.