in this paper, a new approach of modeling for artificial neural networks (ann) models based on the concepts of ann and fuzzy regression is proposed. for this purpose, we reformulated ann model as a fuzzy nonlinear regression model while it has advantages of both fuzzy regression and ann models. hence, it can be applied to uncertain, ambiguous, or complex environments due to its flexibility. in ...

This study presents a model for district-level electricity demand forecasting using a set of Artificial Neural Networks (ANNs) (parallel ANNs) based on current energy loads and social parameters such as occupancy. A comprehensive sensitivity analysis is conducted to select the inputs of the ANN by considering external weather conditions, occupancy type, main income providers’ employment status ...

background: diabetes and hypertension are important non-communicable diseases and their prevalence is important for health authorities. the aim of this study was to determine the predictive precision of the bivariate logistic regression (lr) and artificial neutral network (ann) in concurrent diagnosis of diabetes and hypertension. methods: this cross-sectional study was performed with 12000 ira...

in today’s world renewable energy is highly demanding because of increasing oil and fuelprices, desire and necessity to avert irreversible climate damage and also the unreliability of the non -renewablesources. in renewable energy, water is an important source. previously water is used only for survivalof human being as much as food but no attentionhas been givenen to economical use and conserv...

1. Adwani S, Yadav R, Kumar K, Chandra SR, Pal PK. Neuropsychological profile in early Parkinson’s disease: Comparison between patients with right side onset versus left side onset of motor symptoms. Ann Indian Acad Neurol 2016;19:74-8. 2. van der Hoorn A, Bartels AL, Leenders KL, de Jong BM. Handedness and dominant side of symptoms in Parkinson’s disease. Parkinsonism Relat Disord 2011;17:58-6...

in this article a comparative study for modeling and optimization of 2-methylpropane-2-thiol removal from contaminated soil by ultrasound is investigated. central composite design (ccd) and artificial neural network (ann) were utilized and compared to each other in order to obtain appropriate predicting model with respect to sonication power (w), sonication time (min) and water/reactor volume r...

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...

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...