The present work establishes the use of convolutional neural networks as a generative
model for stochastic processes that are widely present in industrial automation and system …

The aim of this special issue is to present new results on data-driven modelling methods and
their applications in engineering and other fields. As practical systems are inherently …

Real-world data of the operation of control systems is often scarce because experiments are
expensive and time-consuming. We address the problem of synthetic data generation …

Artificial neural networks (NNs) have become the de facto standard in machine learning.
They allow learning highly nonlinear transformations in a plethora of applications. However …

Interest in machine learning is exploding worldwide, both in research and for industrial
applications. Machine learning is fast becoming a fundamental part of everyday life. This …

Spatiotemporal processes, such as temperature in an area, motion of a vehicle, etc., depend
on the spatial features of the underlying phenomena as well as time. Developing models …

This paper presents an approach for optimizing the robustness of production and logistic
systems based on deep generative models, a special method of deep learning. Robustness …

We demonstrate the application of automated machine learning to the problem of identifying
dynamic process models using recurrent neural networks (RNNs). The general concept …

Abstract Machine learning tools are widely used for knowledge extraction, modeling, and
decision tasks; a range of problems that Control Theory also tackles. Their relations have …

Abstract Most Artificial Neural Networks that are widely used today focus on approximating
deterministic input-output mapping of nonlinear phenomena, and therefore, they can be well …