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The paper presents the results of a comparative study performed between two computational intelligence techniques, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) applied to particulate matter (fraction PM2.5) air pollution forecasting. The experiments were realized on datasets from the Airbase databases with PM2.5 hourly measurements. The main statistical parameters...
A predictive models were developed to determine the thermal comfort level for the academic classroom by using artificial neural networks (ANNs). The paper reports experimental and theoretical analysis on a problem of achieving a desired thermal comfort level. The proposed method focused on the classical artificial (feed forward) neural networks (ANN) and the time-series NARX feedback neural networks...
This paper presents the retro-propagation algorithm for tuning the parameter of Artificial Neural Networks used by pharmachemical industry. The numerical test results obtained on lubrication and air circuits shown that the proposal improve the performance in terms of number of iterations and reliability of the models. BEKER Laboratories production line, is a Pharmaceutical production company located...
This paper presents the theoretical analysis, design and simulation of a single phase single stage boost dc-ac converter powered from PV array. The main attribute of the boost inverter topology is the fact that it generates an ac output voltage larger than the dc input one, depending on the instantaneous duty cycle. This paper proposes an accurate solar panel simulation to incorporate the temperature...
Accurate air-conditioning load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. Many forecasting techniques such as support vector machine (SVM), artificial neural network (ANN), autoregressive integrated moving average (ARIMA) and grey model, have been proposed in the field of air-conditioning load prediction. However, none of them has enough accuracy...
This paper reports the application of artificial neural networks for estimating reference evapotranspiration (ETo) as a function of local maximum and minimum air temperatures and exogenous relative humidity and evapotranspiration in twelve coastal locations of the autonomous Valencia region, Spain. The Penman-Monteith model for ETo prediction, as been proposed by the Food and Agriculture Organization...
In order to diagnose prevent and remove the failure of the running equipment, we must use effectual diagnose measure. In this study, By the analysis on the fuzzy theory and the ultimatum of the ANN, establish the fault diagnosis model based on the ANN and FNN. Using the same training sample and examine sample in the emulation based of the ANN and FNN, the result prove that FNN have faster convergent...
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