Artificial neural networks (ANN) have shown to be a powerful tool for system modeling in a wide range of applications. The focus of this study is on neural network applications to data analysis in egg production. An ANN model with two hidden layers, trained with a back propagation algorithm, successfully learned the relationship between the input (age of hen) and output (egg production) variabl...

background: forecasting of air pollutants has become a popular topic of environmental research today. for this purpose, the artificial neural network (aan) technique is widely used as a reliable method for forecasting air pollutants in urban areas. on the other hand, the evolutionary polynomial regression (epr) model has recently been used as a forecasting tool in some environmental issues. in ...

Applying nonlinear models to estimation and forecasting economic models are now becoming more common, thanks to advances in computing technology. Artificial Neural Networks (ANN) models, which are nonlinear local optimizer models, have proven successful in forecasting economic variables. Most ANN models applied in Economics use the gradient descent method as their learning algorithm. However, t...

In this study, it was aimed to develop an accurate forecasting model for the monthly electricity demand of Turkey in medium-term. For purpose, Group Method Data Handling (GMDH)-type Neural Network (NN) approach used structure a nonlinear time-series based model. A large dataset containing considered period 2003-2018. The developed tested 2019/01-2019/11 order determine generalization ability te...

this study was conducted to investigate the prediction of growth performance using linear regression and artificial neural network (ann) in broiler chicken. artificial neural networks (anns) are powerful tools for modeling systems in a wide range of applications. the ann model with a back propagation algorithm successfully learned the relationship between the inputs of metabolizable energy (kca...

in this work the electrooxidation half-wave potentials of some benzoxazines were predicted from their structural molecular descriptors by using quantitative structure-property relationship (qsar) approaches. the dataset consist the half-wave potential of 40 benzoxazine derivatives which were obtained by dc-polarography. descriptors which were selected by stepwise multiple selection procedure ar...

Shear wave velocity (VS) is one of the most important parameters in deep and surface studies estimation geotechnical design parameters. This parameter widely utilized to determine permeability porosity, lithology, rock mechanical parameters, fracture assessment. However, measuring this either impossible or difficult due challenges related horizontal deviation wells difficulty reaching cores. Ar...

This study concerns numerical simulation, modeling and optimization of aerodynamic stall control using a synthetic jet actuator. The numerical simulation was carried out by a large-eddy simulation that employs a RNG-based model as the subgrid-scale model. The flow around a NACA0015 airfoil, including a synthetic jet located at 10 % of the chord, is studied under Reynolds number Re = 12.7 × 10 a...

This paper proposes a new passive robust fault detection scheme using non-linear models that include parameter uncertainty. The nonlinear model considered here is described by a group method of data handling (GMDH) neural network. The problem of passive robust fault detection using models including parameter uncertainty has been mainly addressed by checking if the measured behaviour is inside t...