In this paper, different types of learning networks, such as artificial neural networks (ANNs), Bayesian neural networks (BNNs), support vector machines (SVMs) and Bayesian support vector machines (BSVMs) are applied for tornado forecasting. The last two approaches utilize kernel methods to address nonlinearity of the data in the input space. All methods are applied to forecast when tornadoes o...

In this paper, a new approach of modeling for Artificial Neural Networks (ANNs) models based on the concepts of fuzzy regression is proposed. For this purpose, we reformulated ANN model as a fuzzy nonlinear regression model while it has advantages of both fuzzy regression and ANN models. Hence, it can be applied to uncertain, ambiguous, or complex environments due to its flexibility for forecas...

Review and classification of electric load forecasting (LF) techniques based on artificial neural networks (ANN) is presented. A basic ANNs architectures used in LF reviewed. A wide range of ANN oriented applications for forecasting are given in the literature. These are classified into five groups: (1) ANNs in short-term LF, (2) ANNs in mid-term LF, (3) ANNs in long-term LF, (4) Hybrid ANNs in...

Artificial neural networks (ANNs) have been successfully applied to solve a variety of classification and function approximation problems. Although ANNs can generally predict better than decision trees for pattern classification problems, ANNs are often regarded as black boxes since their predictions cannot be explained clearly like those of decision trees. This paper presents a new algorithm, ...

Artificial neural networks (ANNs) are a form of artificial intelligence and, since the mid-1990s, ANNbased models have been successfully applied to virtually every problem in geotechnical engineering. This paper briefly examines the areas of geotechnical engineering to which ANNs have been applied, provides a brief overview of the operation of ANN models, and highlights and discusses four impor...

Neural Networks are an artificial intelligence method for modeling complex target functions. For certain types of problems, such as learning to interpret complex realworld sensor data, Artificial Neural Networks (ANNs) are among the most effective learning methods currently know. During the last decade they have been widely applied to the domain of financial time series prediction and their imp...

An analytical review of recent publications in the area of digital speech signal processing is presented. The aim of the given paper is the analysis of these publications, where Artificial Neural Networks (ANNs) were successfully employed. Numerous methods of ANNs employment are discussed due to identify when and why they are reliable alternative to the conventional adaptive signal processing t...

Production of highly viscous tar sand bitumen using Steam Assisted Gravity Drainage (SAGD) with a pair of horizontal wells has advantages over conventional steam flooding. This paper explores the use of Artificial Neural Networks (ANNs) as an alternative to the traditional SAGD simulation approach. Feed forward, multi-layered neural network meta-models are trained through the Back-Error-Propaga...

Some of the significant recent advances in the field of artificial neural networks (ANNs) applied to optical fibre sensors are reviewed. Particular attention is given to the use of ANNs in the enhancement of the performance of existing single point sensors, two-and three-dimensional measurements and developments in multipoint sensors and sensor arrays.