Artificial Neural Networks (ANNs) are the nonlinear and adaptive information processing systems which are combined by numerous processing units, with the characteristics of self-adapting, self-organizing and realtime learning, and play an important in pattern recognition, machine learning and data mining. But we’ve encountered many problems, such as the selection of the structure and the parame...

The aim of this study was to investigate the particulate dispersion from Kerman Cement Plant. The upwind – downwind method was used to measure particle concentration and a cascade impactor was applied to determine particle size distribution. An Eulerian model, Gaussian plume model and an artificial neural network have been used to compute and predict concentration of PM10 from Ke...

a local scouring phenomenon is one of the important problems in hydraulic design of groynes. due to constriction and downward flow, the scouring can occur around the groynes. nowadays, the artificial neural networks have a lot of applications in various water engineering problems where there is not any specific relation between effective parameters. in this study, the artificial neural networks...

This work illustrates the use and some results of Artificial Neural Networks (ANNs) for data quality control of air pollutants. ANNs are applied to the short-term predicting of air pollutant concentrations in urban areas. Observed versus predicted data are compared to test the efficacy of ANNs in simulating environmental processes. Statistical analysis is used for choice of neural structure. Th...

Artificial Neural Networks (ANNs) have been successfully applied for pattern recognition, speech recognition, control and other real world problems. This paper presents a method for segmentation of printed and difficult handwritten postal addresses. The segmentation algorithm is used to prepare raw training data for use with an Artificial Neural Network. The C programming language, the SP2 supe...

The aim of this paper is to examine the efficiency of two credit risk modeling (CRM) to predict the credit risk of commercial Iranian banks: (1) Logistic regression model (LRM); (2) Artificial neural networks (ANNs). The calculations have been done by using SPSS and MATLAB software. Number of samples was 316 and 5 dependent variables. The results showed that, artificial neural network is more p...

This article describes how artificial neural networks (ANNs) can be used to benefit a number of RF and microwave measurement areas including vector network analysis (VNA). We apply ANNs to model a variety of on-wafer and coaxial VNA calibrations, including open-short-load-thru (OSLT) and line-reflect-match (LRM), and assess the accuracy of the calibrations using these ANN-modeled standards. We ...

Forecasting a hydrologic time series has been one of the most complicated tasks owing to the wide range of data, the uncertainties in the parameters influencing the time series and also due to the non availability of adequate data. Recently, Artificial Neural Networks (ANNs) have become quite popular in time series forecasting in various fields. This paper demonstrates the use of ANNs to foreca...