in this study, artificial neural network was used to predict the surface tension of 20 hydrocarbon mixtures. experimental data was divided into two parts (70% for training and 30% for testing). optimal configuration of the network was obtained with minimization of prediction error on testing data. the accuracy of our proposed model was compared with four well-known empirical equations. the arti...

This paper presents a feed forward back-propagation neural network model to predict the retained tensile strength and design chart in order to estimation of the strength reduction factors of nonwoven geotextiles due to installation process. A database of 34 full-scale field tests were utilized to train, validate and test the developed neural network and regression model. The results show that t...

quantitative prediction of municipal solid waste generation has an important role in the optimization and programming of municipal solid waste management system. but, this concept was companied with many problems, because of the non homogenous nature and the effect of various factors out of the control on solid waste generation. in this study, the combination of artificial neural network and wa...

In this paper, we introduce a hybrid approach based on neural network and optimization teqnique to solve ordinary differential equation. In proposed model we use heyperbolic secont transformation function in hiden layer of neural network part and bfgs teqnique in optimization part. In comparison with existing similar neural networks proposed model provides solutions with high accuracy. Numerica...

in recent years, the existing competitions between investment companies have been increased largely by entering private investors in capital market. large and powerful companies try to achieve the goals predicted to increase the competition capacity. to analyze the efficiency of investment companies, parametric and non-parametric methods are used. in this research, based on the dissociation pow...

we compare two approaches for a markovian model in flexible manufacturing systems (fmss) using monte carlo simulation. the model which is a development of fazlollahtabar and saidi-mehrabad (2013), considers two features of automated flexible manufacturing systems equipped with automated guided vehicle (agv) namely, the reliability of machines and the reliability of agvs in a multiple agv jobsho...

drought forecasting in khash city by using neural network model hossein negaresh associate professor of geography and environmental planningfaculty, university of sistan & baluchestan mohsen armesh holding master degree in climatology in environmental planning extended abstract 1- introduction drought is condition of lack of rainfall and increase in temperature occurring in any climatic condit...

evaporation is one of the most important components of hydrologic cycle.accurate estimation of this parameter is used for studies such as water balance,irrigation system design, and water resource management. in order to estimate theevaporation, direct measurement methods or physical and empirical models can beused. using direct methods require installing meteorological stations andinstruments ...