Adaptive neo-fuzzy bagging metamodel and its fast learning

Adaptive nonlinear neo-fuzzy bagging metamodel and its fast online learning procedure are proposed intended for optimal combining of the results of different computational intelligence systems that simultaneously solve the same problem.It is assumed that the data is processed by the members of the ensemble and by the metamodel online in real time.The proposed metamodel is intended for solving a wide class of Data Stream Mining problems under the conditions of data non-stationarity, and when the processing speed is of the utter importance.Simulation based on the short-term electric load forecasting problem confirmed theoretical results.The metamodel demonstrated significant improvement over the results of the member models.