In this study, an artificial neural network was used to predict the minimum force required to single point incremental forming (SPIF) of thin sheets of Aluminium AA3003-O and calamine brass Cu67Zn33 alloy. Accordingly, the parameters for processing, i.e., step depth, the feed rate of the tool, spindle speed, wall angle, thickness of metal sheets and type of material were selected as input and t...

this paper presents a model predictive control (mpc) approach for production and inventory control systems. model predictive control previously has been successfully applied to supply chain problems; however most systems that have been proposed so far possess no information on future demand. the incorporation of a forecasting methodology in an mpc framework can promote the efficiency of control...

abstract the aim of this study was to selecting the suitable model for forecast land, production and price of sugar beet in iran. for this purpose, models applied to forecast are arima, single and double exponential smoothing, harmonic, artificial neural network and arch for period 1993-2008. results of durbin-watson tests, land, production and price of sugar beet series were found non stochast...

on one hand, oil is the greatest energy resource in the world and, on the other hand, because of the role of oil revenue in the economic of oil producer countries, such as iran,it is vital for these countries. so it is necessary to recognize different affective parameters on oil market for these countries. in this research, we try to forecast oil price as an important variable in world wide oil...

investigation of soil properties like cation exchange capacity (cec) plays important roles in study of environmental reaserches as the spatial and temporal variability of this property have been led to development of indirect methods in estimation of this soil characteristic. pedotransfer functions (ptfs) provide an alternative by estimating soil parameters from more readily available soil data...

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...

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...

Cox regression model serves as a statistical method for analyzing the survival data, which requires some options such as hazard proportionality. In recent decades, artificial neural network model has been increasingly applied to predict survival data. This research was conducted to compare Cox regression and artificial neural network models in prediction of kidney transplant survival. The prese...