Towards Robust and Process-Save EFS
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Doing incremental and evolving learning in a way, such that the performance of the evolved models are close to the (hypothetical) batch solution (achieved by collecting all the data and loading them at once into the training algorithms). From mathematical point of view, this means that a kind of convergence of some parameters to an optimality criterion is achieved.
Implementing approaches to assure a high performance of the models with respect to accuracy, correctness and stability also in specific cases occurring in the incoming data streams or in the learning procedure itself. The former can be caused by specific situations in the environmental system (e.g. a changing or faulty system behavior) where the data is generated.
Instabilities in the learning process (Section 4.1)
Upcoming drifts and shifts in on-line data streams (Section 4.2)
Situations causing an unlearning effect (Section 4.3)
Outliers and faults in on-line data streams (Section 4.4)
Uncertainties in the models due to high noise levels or lack of data (Section 4.5)
Extrapolation situations for new samples to be predicted (Section 4.6)
KeywordsFuzzy System Fuzzy Model Consequent Parameter Optimal Regularization Parameter Fuzzy Regression Model
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