Nonlinear system identification is an extremely broad topic, since every system that is not
linear is nonlinear. That makes it impossible to give a full overview of all aspects of the fi eld …

The human nervous system is one of the most complicated systems in nature. Complex
nonlinear behaviours have been shown from the single neuron level to the system level. For …

Abstract Using Artificial Neural Networks (ANN) for nonlinear system identification has
proven to be a promising approach, but despite of all recent research efforts, many practical …

Deep learning is a topic of considerable current interest. The availability of massive data
collections and powerful software resources has led to an impressive amount of results in …

Tensor decompositions can be a powerful tool when faced with the curse of dimensionality
and have been applied in myriad applications. Their application to problems in the 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 …

Regularised kernel-based identification methods are widely used for large-scale systems
with the aim of reducing high parameter estimation variances. Classical diagonal and …

This manuscript presents a novel approach to nonlinear system identification leveraging
densely defined Liouville operators and a new" kernel" function that represents an …

The challenge for next generation system identification is to build new flexible models and
estimators able to simulate complex systems. This task is especially difficult in the nonlinear …

The sequential stabilizing spline (SSS) algorithm is a remarkable algorithm for identifying
linear dynamical systems. It can guarantee the stability of an estimated model by polishing …