Linearization and Identification of Multiple-Attractor Dynamical Systems through Laplacian Eigenmaps. (arXiv:2202.09171v2 [cs.LG] UPDATED)

Dynamical Systems (DS) are fundamental to the modeling and understanding time
evolving phenomena, and have application in physics, biology and control. As
determining an analytical description of the dynamics is often difficult,
data-driven approaches are preferred for identifying and controlling nonlinear
DS with multiple equilibrium points. Identification of such DS has been treated
largely as a supervised learning problem. Instead, we focus on an unsupervised
learning scenario where we know neither the number nor the type of dynamics. We
propose …

arxiv identification linearization multiple systems