The black-box impedance model of voltage source inverters (VSIs) can be measured at their terminals without access to internal control details, which greatly facilitate the analysis inverter-grid interactions. However, VSI is dependent on its operating point and have different profiles when changed. This letter proposes a method for identifying under wide range points. approach based artificial...

Modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to itsessential application to water resources management. Recently, artificial intelligence has gained much popularity owing toits application in calibrating the nonlinear relationships inherent in the stream flow–suspended sediment relationship. Thisstudy made us of adaptive neuro-fuzzy ...

to determine the amount of food amino acid and to spend time in the laboratories are expensive & time-consuming due to a chemical analysis. in the current laboratories, digestion nirs method is widely used for this purpose. but this method has technical limitation. therefor is important find appropriate method for estimate amount of amino acids. artificial neural network (ann) can provide a bet...

computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. nowadays, despite the numerous time series forecasting models propos...

Dissolved organic carbon concentrations ([DOC]) in surface waters are increasing in many regions of Europe and North America. These increases are likely driven by a combination of changing climate, recovery from acidification and change in severity of winter storms in coastal areas. INCA-C, a process-based model of climate effects on surface water [DOC], was used to explore the mechanisms by wh...

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...

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...

nowadays, due to increasing the complexity of ic engines, calibration task becomes more severe and the need to use surrogate models for investigating of the engine behavior arises. accordingly, many black box modeling approaches have been used in this context among which network based models are of the most powerful approaches thanks to their flexible structures. in this paper four network base...

modelling and forecasting stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. this nonlinearity affects the efficiency of the price characteristics. using an artificial neural network (ann) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

Estimation of the nonlinear buckling capacity of thin walled shells is one of the most important aspects of structural mechanics. In this study the axial buckling load of 132 stiffened shells were numerically calculated. The applicability of artificial neural networks (ANN) in predicting the buckling capacity of vertically stiffened shells was studied. To this end feed forward (FF) multi-layer ...