infiltration rate is one of the most important soil physical parameters and is a basic input data in irrigation and drainage projects. although, a number of theoretical or experimental based equations are presented to describe this phenomenon but the evaluation of some new sciences such as artificial neural networks, for prediction of the phenomenon can be investigated. generally, the infiltrat...

We consider the situation where a nonlinear physical system is identiied from input-output data. In case no speciic physical structural knowledge about the system is available, parameterized grey box models cannot be used. Identiication in black-box-type of model structures is then the only alternative, and general approaches like neural networks, wavelet models, neuro-fuzzy models, etc., have ...

in this study the wavelet neural network (wnn) and artificial neural network (ann) were used to simulate barley breakage percentage in combine harvester. the models have been trained using the same data conditions. air temperature, thresher cylinder speed, distance between thresher cylinder and concave (back and forth) and the percentage of barely moisture were as the input variables. the resul...

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...

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 develops a trajectory controller network (TCN), formed with a set of simple linear blocks, for nonlinear control. Utilising linear controller synthesis techniques, the network and its nodes are straightforward to interpret and apply, unlike a neural network or fuzzy logic system, which need excessive network elements and weighting with physical meanings indirect to interpret. The TCN...

Early detection of glaucoma is important to slow down progression of the disease and to prevent total vision loss. We have been studying an automated scheme for detection of a retinal nerve fiber layer defect (NFLD), which is one of the earliest signs of glaucoma on retinal fundus images. In our previous study, we proposed a multi-step detection scheme which consists of Gabor filtering, cluster...

Neural networks are good at classification, forecasting and recognition. They are also good candidates of financial forecasting tools. Forecasting is often used in the decision making process. Neural network training is an art. Trading based on neural network outputs, or trading strategy is also an art. We will discuss a seven-step neural network forecasting model building approach in this arti...

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...