Selective ensemble of multiple local model learning for nonlinear and nonstationary systems

Abstract

This paper proposes a selective ensemble of multiple local model learning for modeling and identification of nonlinear and nonstationary systems, in which the set of local linear models are self adapted to capture the newly emerging process characteristics and the prediction of the process output is also self adapted based on an optimally selected ensemble of subset linear local models. Specifically, our selective ensemble of multiple local model learning approach performs the model adaptation at two levels. At the level of local model adaptation, a newly emerging process state in the incoming data is automatically identified and a new local linear model is fitted to this newly emerged process state. At the level of online prediction, a subset of candidate local linear models are optimally selected and the prediction of the process output is computed as an optimal linear combiner of the selected subset local linear models. Two case studies involving chaotic time series prediction and modeling of a real-world industrial microwave heating process are used to demonstrate the effectiveness of our proposed approach, in comparison with other existing methods for modeling and identification of nonlinear and time-varying systems.