A functional relationship between two variables, applied mass to a weighing platform and estimated mass using Multi-Layer Perceptron Artificial Neural Networks is approximated by a linear function. Linear relationships and correlation rates are obtained which quantitatively verify that the Artificial Neural Network model is functioning satisfactorily. Estimated mass is achieved through recallin...

The paper deals with Data Envelopment Analysis (DEA) and Artificial Neural Network (ANN). We believe that solving for the DEA efficiency measure, simultaneously with neural network model, provides a promising rich approach to optimal solution. In this paper, a new neural network model is used to estimate the inefficiency of DMUs in large datasets.

In this study, we evaluate the accuracy of classifiers for classification of ultrasonic liver tissues. Two different statistic classifiers ami three various artificial neural networks are included: Bayes classifier k-nearest neighbor classifier. Back-propagation neural networks, probabilistic neural network and modified probabilistic neural network. These Jive different classifiers were investi...

Received Nov 07, 2016 Revised Jan 13, 2017 Accepted Jan 23, 2017 An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentr...

introduction: repetitive strain injuries are one of the most prevalent problems in occupational diseases. repetition, vibration and bad postures of the extremities are physical risk factors related to work that can cause chronic musculoskeletal disorders. repetitive work on a computer with low level contraction requires the posture to be maintained for a long time, which can cause muscle fatigu...

The goal of this research is to predict total stock market index of Tehran Stock Exchange, using the compound method of ARIMA and neural network in order for the active participations of finance market as well as macro decision makers to be able to predict trend of the market. First, the series of price index was decomposed by wavelet transform, then the smooth's series  predicted by using...

background: one issue of concern in water supply is the quality of water. measuring the qualitative parameters of water is time-consuming and costly. predicting these parameters using various models leads to a reduction in related expenses and the presentation of overall and comprehensive statistics for water resource management. methods: the present study used an artificial neural network (ann...

gas hydrates are a costly problem when they plug oil and gas pipelines. the best way to determine the hft and pressure is to measure these conditions experimentally for every gas system. since this is not practical in terms of time and money, correlations are the other alternative tools. there are a small number of correlations for specific gravity method to predict the hydrate formation. as th...

one of the main aims of water resource planners and managers is to estimate and predict the parameters of groundwater quality so that they can make managerial decisions. in this regard, there have many models developed, proposing better management in order to maintain water quality. most of these models require input parameters that are either hardly available or time-consuming and expensive to...

a neural network is developed for the determination of leaky confined aquifer parameters. leakage into the aquifer takes place from the storage in the confining aquitard. the network is trained for the well function of leaky confined aquifers by the back propagation technique and adopting the levenberg–marquardt optimization algorithm. by applying the principal component analysis (pca) on the a...