Dynamical systems see widespread use in natural sciences like physics, biology, and
chemistry, as well as engineering disciplines such as circuit analysis, computational fluid …

Cyclostratigraphy is an important tool for understanding astronomical climate forcing and
reading geological time in sedimentary sequences, provided that an imprint of insolation …

We propose a methodology for the identification of nonlinear state–space models from
input/output data using machine-learning techniques based on autoencoders and neural …

The prediction model is the most important part of an MPC strategy. The accuracy of such a
model influences the quality of predictions and control performance of the algorithm. In some …

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 …

We introduce a new methodology for the identification of nonlinear state-space models
using machine-learning techniques based on deep autoencoders for dimensionality …

Featured Application Underwater vehicle control surfaces motor control. Abstract Adaptive
and learning methods are proposed and compared to control DC motors actuating control …

Volterra series approximate a broad range of nonlinear systems. Their identification is
challenging due to the curse of dimensionality: the number of model parameters grows …

Neural differential equations have recently emerged as a flexible data-driven/hybrid
approach to model time-series data. This letter experimentally demonstrates that if the data …

The identification of black-box nonlinear statespace models requires a flexible
representation of the state and output equation. Artificial neural networks have proven to …