The advancement of the Internet of Things (IoT) has increased the demand for large-scale
intelligent sensing systems. The periodic replacement of power sources for ubiquitous …

In the absence of knowledge about challenging dynamic phenomena involved in batch
distillation processes, eg, complex flow regimes or appearing and vanishing phases …

This doctoral thesis aims to establish a theoretically-sound framework for the adoption of
Recurrent Neural Network (RNN) models in the context of nonlinear system identification …

This paper proposes a novel recurrent context layered radial basis function neural network
(RCLRBFNN) for the identification of nonlinear dynamical systems. The proposed model …

This research explores the application of the Riemann–Liouville fractional sigmoid, briefly
RLF σ, activation function in modeling the chaotic dynamics of Chua's circuit through …

We consider the problem of designing a machine learning-based model of an unknown
dynamical system from a finite number of (state-input)-successor state data points, such that …

This paper presents an end-to-end learning approach to developing a Nonlinear Model
Predictive Control (NMPC) policy, which does not require an explicit first-principles model …

Effective quantification of uncertainty is an essential and still missing step towards a greater
adoption of deep-learning approaches in different applications, including mission-critical …

The accurate and robust prediction of remaining useful life (RUL) is critical for enabling the
proactive mitigation of fault effects rather than reacting to them. For RUL prediction, one must …

In the context of human-robot collaboration (HRC), the model of the robots needs to be
adapted to describe new tasks in new environments and under new operating conditions. A …