the objective of this paper is to develop an artificial neural network (ann) model which can beused to predict temperature rise due to climate change in regional scale. in the present work data recorded overyears 1985-2008 have been used at training and testing steps for ann model. the multilayer perceptron(mlp) network architecture is used for this purpose. three applied optimization methods a...

An efficient methodology is proposed to find optimal shape of arch dams on the basis of constrained natural frequencies. The optimization is carried out by virtual sub population (VSP) evolutionary algorithm employing real values of design variables. In order to reduce the computational cost of the optimization process, the arch dam natural frequencies are predicted by properly trained back pro...

in this paper, i develop three forecasting models: namely structural, times series, and artificial neural networks; to forecast iranian inflation rates. the structural model uses aggregate demand and aggregate supply approach, the time series model is based on the standard arlma technique, and the artificial neural network applies multi-layer back propagation model the latter, which is rooted i...

estimation of the metal value in the metallic deposits is one of the important factors to evaluate the deposits in exploration studies andmineral processing. therefore, one accurate estimator is essential to obtain a fine insight into the accumulation of the ore body. thereare geostatistical methods for the estimation of the concentration of iron which performance of these models is complexity ...

the objective of this paper is to develop an artificial neural network (ann) model which can beused to predict temperature rise due to climate change in regional scale. in the present work data recorded overyears 1985-2008 have been used at training and testing steps for ann model. the multilayer perceptron(mlp) network architecture is used for this purpose. three applied optimization methods a...

For many classiication tasks, the set of available task instances can be roughly divided into regular instances and exceptions. We investigate three learning algorithms that apply a diierent method of learning with respect to regularities and exceptions, viz. (i) back-propagation, (ii) cascade back-propagation (a constructive version of back-propagation), and (iii) information-gain tree (an ind...

In this paper, performance of three classifiers for classification of five mental tasks were investigated. Wavelet Packet Transform (WPT) was used for feature extraction of the relevant frequency bands from raw Electroencephalograph (EEG) signal. The three classifiers namely used were Multilayer Back propagation Neural Network, Support Vector Machine and Radial Basis Function Neural Network. In...

The back propagation algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a training algorithm consisting of a learning rate and a momentum factor. The major drawbacks of above learning algorithm are the problems of local minima and slow convergence speeds. The addition of an extra term, called a proportional factor reduces the convergence of th...

The present work investigates the significance of arithmetic precision in neural network simulation. Noting that a biological brain consists of a large number of cells of low precision, we try to answer the question: With a fixed size of memory and CPU cycles available for simulation, does a larger sized net with less precision perform better than smaller sized one with higher precision? We eva...