<ul><li>This paper introduces a structured neural network (StNN) for realizing states of dynamical systems by leveraging data-driven learning.</li><li>The StNN utilizes a low-complexity operator called the Hankel operator, derived from time-delay measurements, to solve dynamical systems.</li><li>Numerical simulations comparing the StNN with other techniques show that it reduces the number of parameters and computational complexity.</li><li>The proposed StNN enables the prediction and understanding of future states in state-space dynamical systems.</li></ul>

A Low-complexity Structured Neural Network to Realize States of Dynamical Systems

Discover more