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...

In the paper the inference machine model for vocation psychology was build and developed by a rule-based rough set theory. At first, the rough set is used to optimize the rules for career psychological identification, by which the complexity of the neural network can be avoided. Second, the features used by the questionnaires are selected for input parameters of the classifier to incorporate mo...

in the near future the use of distributed generation systems will play a big role in the production ofelectrical energy. one of the most common types of dg technologies , fuel cells , which can be connectedto the national grid by power electronic converters or work alone studies the dynamic behavior andstability of the power grid is of crucial importance. these studies need to know the exact mo...

Artificial neural networks are intelligent systems that have successfully been used for prediction in different medical fields. In this study, the efficiency of a neural network for predicting the survival of patients with acute pancreatitis is compared with days-of-survival obtained from patients. A three- layer back-propagation neural network was developed for this purpose. Clinical data (e.g...

â â â â â â â  this paper proposes a new forecasting model for investigating relationship between the price of crude oil, as an important energy source and gdp of the us, as the largest oil consumer, and the uk, as the oil producer. gmdh neural network and mlff neural network approaches, which are both non-linear models, are employed to forecast gdp responses to the oil price changes. the resul...

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...

bedload transport is an essential component of river dynamics and estimation of its rate is important to many aspects of river management. in this study, measured bedload by helley- smith sampler was used to estimate the bedload transport of kurau river in malaysia. an artificial neural network, genetic programming and a combination of genetic programming and a neural network were used to estim...

The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...

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...