This paper addresses the task of identification of nonlinear dynamic systems from measured data. The discrete-time variant of this task is commonly reformulated as a regression problem. As tree ensembles have proven to be a successful predictive modeling approach, we investigate the use of tree ensembles for solving the regression problem. While different variants of tree ensembles have been proposed and used, they are mostly limited to using regression trees as base models. We introduce ensembles of fuzzified model trees with split attribute randomization and evaluate them for nonlinear dynamic system identification. Models of dynamic systems which are built for control purposes are usually evaluated by a more stringent evaluation procedure using the output, i.e., simulation error. Taking this into account, we perform ensemble pruning to optimize the output error of the tree ensemble models. The proposed Model-Tree Ensemble method is empirically evaluated by using input–output data disturbed by noise. It is compared to representative state-of-the-art approaches, on one synthetic dataset with artificially introduced noise and one real-world noisy data set. The evaluation shows that the method is suitable for modeling dynamic systems and produces models with comparable output error performance to the other approaches. Also, the method is resilient to noise, as its performance does not deteriorate even when up to 20% of noise is added. 
Model-Tree Ensembles for noise-tolerant system identification