This paper presents tailor-made neural model structures and two custom fitting criteria for learning dynamical systems. The proposed framework is based on a representation of the system behavior in terms continuous-time state-space models. sequence hidden states optimized along with network parameters order to minimize difference between measured estimated outputs, at same time guarantee that s...