Artificial Neural Networks and Statistical Model

Statistical downscaling with artificial neural networks

Statistical downscaling methods seek to model the relationship between large scale atmospheric circulation, on say a European scale, and climatic variables, such as temperature and precipitation, on a regional or subregional scale. Downscaling is an important area of research as it bridges the gap between predictions of future circulation generated by General Circulation Models (GCMs) and the e...

Statistical Properties of Artificial Neural Networks

Convergence properties of empirically estimated neural networks are examined. In this theory, an appropriate size feedforward network is automatically determined from the data. The networks we study include two and three layer networks with an increasing number of simple sigmoidal nodes, multiple layer polynomial networks, and networks with certain fixed structures but an increasing complexity ...

Statistical inference: learning in artificial neural networks.

Artificial neural networks (ANNs) are widely used to model low-level neural activities and high-level cognitive functions. In this article, we review the applications of statistical inference for learning in ANNs. Statistical inference provides an objective way to derive learning algorithms both for training and for evaluation of the performance of trained ANNs. Solutions to the over-fitting pr...

Performance evaluation of artificial neural networks in statistical downscaling of monthly precipitation (Case study: Minab watershed)

Hourly Wind Speed Prediction using ARMA Model and Artificial Neural Networks

In this paper, a comparison study is presented on artificial intelligence and time series models in 1-hour-ahead wind speed forecasting. Three types of typical neural networks, namely adaptive linear element, multilayer perceptrons, and radial basis function, and ARMA time series model are investigated. The wind speed data used are the hourly mean wind speed data collected at Binalood site in I...