This paper investigates the recursive parameter and state estimation algorithms for a special
class of nonlinear systems (ie, bilinear state space systems). A state observer-based …

Recent developments within deep learning are relevant for nonlinear system identification
problems. In this paper, we establish connections between the deep learning and the …

Nonlinear state-space identification for dynamical systems is most often performed by
minimizing the simulation error to reduce the effect of model errors. This optimization …

Abstract Gaussian Processes (GPs) models have been successfully applied to the problem
of learning from sequential observations. In such context, the family of Recurrent Gaussian …

This paper presents a record of the participation of the authors in a workshop on nonlinear
system identification held in 2016. It provides a summary of a keynote lecture by one of the …

Identification of nonlinear dynamic systems remains a significant challenge across
engineering. This work suggests an approach based on Bayesian filtering to extract and …

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 present paper deals with the identification of nonlinear mechanical vibrations. A grey-
box, or semi-physical, nonlinear state-space representation is introduced, expressing the …

GRAPE is an implementation of Grammatical Evolution (GE) in DEAP, an Evolutionary
Computation framework in Python, which consists of the necessary classes and functions to …

Within the offshore industry Structural Health Monitoring remains a growing area of interest.
The oil and gas sectors are faced with ageing infrastructure and are driven by the desire for …