In the paper a new approach to fuzzy confidence interval identification is presented. The method combines a fuzzy identification methodology with some ideas from statistics. The idea is to find, on a finite set of measured data, the confidence interval defined by the lower and upper fuzzy bound that define the band that contains all the output measurements. The fuzzy confidence interval model can be used in process monitoring, fault detection or in the case of robust control design.
COBISS.SI-ID: 6931284
The objective of the present work was to study the influence of particle size (and indirectly, the influence of dry granulation process) and the settings of the tableting parameters on the tablet capping tendency. Artificial neural network and fuzzy models were used for modelling the effect of the particle size and the tableting machine settings on the capping coefficient. The suitability of routinely measured quantities of tablet quality were tested. Results showed that model-based expert systems can significantly improve the trial-and-error procedures.
COBISS.SI-ID: 7095124
Original research paper in the first quarter of the SCI journals, describing the modeling of the electrical and hydraulical processes in the electric arc furnace and their validation on the EAF operational data.
COBISS.SI-ID: 8159572
In this original research paper the predicitve control approach based on adaptive fuzzy model is developed. This approach can be used in the case of nonlinear and time-variant dynamics. This type of system is also a hybrid semi-batch reactor.
COBISS.SI-ID: 7677524
In this original paper the method of optimal cooperative collision avoidance between multiple robots based on Bernstein-Bézier curves is developed.
COBISS.SI-ID: 7315796