Some authors advocate artificial neural networks as a replacement for statistical forecasting and decision models; other authors are concerned that artificial neural networks might be oversold or just a fad. In this paper we review the literature comparing artificial neural networks and statistical models, particularly in regression-based forecasting, time series forecasting, and decision makin...

This paper presents a review of applications of artificial neural networks in biomedical engineering area. Artificial neural networks in general are explained; some limitations and some proven benefits of neural networks are discussed. Use of art ificial neural network techniques in various biomedical engineering applications is summarised. A case study is used to demonstrate the efficacy of ar...

In this study , after a new Artificial Intelligence technique inspired from the human immune system –the Artificial Immune System (AIS) is described, it is compared with the Artificial Neural Networks (ANNs) and some immune approaches in neural networks are emphasized as application. After the artificial intelligence (AI) has come into existence, especially artificial neural networks took a gre...

abstract process of evapotranspiration (eto) is a major component of the hydrologic cycle that its accurate estimation plays an important role to achieve sustainable development in water balance, irrigation system design and planning and management of water resources. being a function of different metrological parameters and their interactions, evapotranspiration is a complex, nonlinear phenome...

the use of neural networks methodology is not as common in the investigation and pre-diction noise as statistical analysis. the application of artificial neural networks for pre-diction of power tiller noise is set out in the present paper. the sound pressure signals for noise analysis were obtained in a field experiment using a 13-hp power tiller. during measurement and recording of the sound ...

In the present study Iran’s rice imports trend is forecasted, using artificial neural networks and econometric methods, during 2009 to 2013, and their results are compared. The results showed that feet forward neural network leading with less forecast error and had better performance in comparison to econometric techniques and also, other methods of neural networks, such as Recurrent networks a...

the purpose of this study was to evaluate three models of artificial neural networks (ann), regression trees (m5) and hargrives-samani (hg) in estimation of reference evapotranspiration. for this purpose was used climate information of sistan va baloochestan, kerman, yazd and khorasan jonoobi from 1998 to 2008. in addition to effect of wind (u) on evapotranspiration (et0), estimation of et0 was...