Adaptive Online Bagging Using Double Neo-Fuzzy Neuron

A nonlinear online bagging method is developed for integrating multiple systems in real-time data processing tasks. This approach utilizes a double neo-fuzzy neuron metamodel, which enhances approximation capabilities for combining systems outputs effectively. The training algorithm ensures efficient parameter tuning and supports real-time learning (online mode). Testing on short-term electric load forecasting demonstrated the effectiveness of this method, confirming its value in improving prediction accuracy through ensembling.