Nonlinear system identification of complex and nonlinear unit operations and unit processes
requires accurate modelling approaches. For this, first-principles based models were initially …

For accurate prediction of size distribution, production rate and energy requirement,
modelling of industrial grinding circuits (IGC) is essential. However, the complex physical …

For accurate prediction of size distribution, production rate and energy requirement,
modelling of industrial grinding circuits (IGC) is essential. However, the complex physical …

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

System identification is a process where a mathematical model is derived in order to explain
dynamical behaviour of a system. One of its step is model structure selection and it is crucial …

Abstract Model structure selection is a problem in system identification which addresses
selecting an adequate model ie a model that has a good balance between parsimony and …

System identification is a class of control system engineering that determines physical
functionality of a plant and represents them in the form of mathematical expression by …

Neural networks (NNs) and fuzzy systems are commonly used and popular general
modeling methods to handle with many engineering and science problems. These …

Abstract System identification is the field of systems mathematical modeling from
experimental data. In the modeling chain, experiments realization, model structure selection …

The identification of non-linear systems using only observed finite datasets has become a
mature research area over the last two decades. A class of linear-in-the-parameter models …