Learning and system modeling via Hamiltonian neural networks

Abstract

Hamiltonian Neural Networks based orthogonal filters are universal signal processors. The structure of such processors rely on family of Hurwitz-Radon matrices. To illustrate, we propose in this paper a procedure of nonlinear mapping synthesis. Hence, we propose here system modeling and learning architectures which are suitable for very large scale implementations.