the main task of this work is related with the design of a class of siso robust control law for theregulation of substrate concentration (cdo) of an industrial activated sludge wastewater plant. the controldesign is related with an uncertainty estimator (reduced order observer) based active control. departing from the tracking error between the desired and the current substrate concentrations t...

the main task of this work is related with the design of a class of siso robust control law for theregulation of substrate concentration (cdo) of an industrial activated sludge wastewater plant. the controldesign is related with an uncertainty estimator (reduced order observer) based active control. departing from the tracking error between the desired and the current substrate concentrations t...

this paper presents an indirect adaptive system based on neuro-fuzzy approximators for the speed control of induction motors. the uncertainty including parametric variations, the external load disturbance and unmodeled dynamics is estimated and compensated by designing neuro-fuzzy systems. the contribution of this paper is presenting a stability analysis for neuro-fuzzy speed control of inducti...

the current study addresses an estimation of investor's optimal portfolio under conditions of uncertainty by using a combination of artificial neural network and markowitz models. for this purpose, such assets as stock prices, house prices, coin and bonds price are used with monthly data over the period 1378-1392. three variables including inflation uncertainty, oil uncertainty and free ma...

In this paper, a new method for modelling and estimation of reliability parameters of power transformer components in distribution and transmission voltage levels for preventive-corrective maintenance schedule of transformers is proposed. In this method, with optimal estimation of Weibull distribution parameters using least squares method and input data uncertainty reduction, failure rate and p...

In recent years fully-parametric fast simulation methods based on generative models have been proposed for a variety of high-energy physics detectors. By their nature, the quality data-driven degrades in regions phase space where data are sparse. Since machine-learning hard to analyse from physical principles, commonly used testing procedures performed way and can't be reliably such regions. ou...

To determine the geophysical structure of a region, we measure seismic travel times and reconstruct velocities at different depths from this data. There are several algorithms for solving this inverse problem, but these algorithms do not tell us how accurate these reconstructions are. Traditional approach to accuracy estimation assumes that the measurement errors are independently normally dist...

In every production plant, it is necessary to have an estimation of production level. Sometimes there are many parameters affective in this estimation. In this paper, it tried to find an appropriate estimation of production level for an industrial factory called Barez in an uncertain environment. We have considered a part of production line, which has different production time for different kin...