Identification of autonomous complex dynamic systems from noisy data

In this paper, we study the data driven identification of large scale dynamic systems that exhibit complex behavior that manifests itself as multi-modal dynamic behavior. As the first result, we present the identification approach of autonomous stochastic dynamic systems. The resulting model is hybrid in nature. We detect the multi-modal dynamics as well as local dynamics within each mode, thus providing a complete unified approach of identification of the system dynamics. Simulation examples are carried out to illustrate the effectiveness of the presented algorithm.