this paper describes two data analysis techniques adopted in a decision support system (dss), decline curve estimation and artificial neural network (ann) approaches, which aid users in predicting oil production of a field. the system generates different predictions, according to scenario, chosen for prediction. these two approaches show that to explain production of a field, ann method shows b...

Constitutive modeling of clay is an important research in geotechnical engineering. It is difficult to use precise mathematical expressions to approximate stress-strain relationship of clay. Artificial neural network (ANN) and support vector machine (SVM) have been successfully used in constitutive modeling of clay. However, generalization ability of ANN has some limitations, and application of...

P. DELMONT 1,2†, R. KEPPENS 1,2,3,4 AND B. v an d e r HOLST 5 1 Centre for Plasma Astrophysics, K.U.Leuven, Heverlee, Belgium 2 Leuven Mathematical Modeling and Computational Science Centre, Heverlee, Belgium 3 Astronomical Institute, Utrecht University, The Netherlands 4 FOM institute for Plasma Physics Rijnhuizen, Nieuwegein, The Netherlands 5 Centre for Space Environment Modeling, Ann Arbor ...

NaL, al Postgraduate School, Monterey, CA, USA of formal and informal logic to practial problems, especially problems in management science, broadly construed. This third special issue of Steven O. Kimbrough Decision Support Systems devoted to logic modeling ~ appears at a happy time for the field. Logic Uniuersity of Michigan, School of Business Administration, Ann modeling is established, rec...

This paper proposes an overview of the modeling process of artificial neural networks (ANN) in stock movement prediction. A step-by-step procedure based on the most commonly used methods is presented, showing the difficulties encountered when modeling such neural networks. Other techniques are also mentioned as neural networks are not the only tools used to predict stock movements.

background: although experiments on water quality are time consuming and expensive, models are often employed as supplement to simulate water quality. artificial neural network (ann) is an efficient tool in hydrologic studies, yet it cannot predetermine its results in the forms of maps and geo-referenced data. methods: in this study, ann was applied to simulate groundwater quality and geographi...

This paper presents an artificial neural network (ANN) for modeling multicategorical land use changes. Compared to conventional statistical models and cellular automata models, ANNs have both the architecture appropriate for addressing complex problems and the power for spatio-temporal prediction. The model consists of two layers with multiple input and output units. Bayesian regularization was...

In this study, Artificial Neural Network (ANN) and Simulated Annealing (SA) techniques were integrated labeled as integrated ANN-SA to estimate optimal process parameters in abrasive waterjet (AWJ) machining operation. The considered process parameters include traverse speed, waterjet pressure, standoff distance, abrasive grit size and abrasive flow rate. The quality of the cutting of machined-...

This paper presents an Artificial Neural Network (ANN) model for failure prediction of critical electronic systems in power plants. Reliability modeling of electronic circuits can be best performed by the stressor – susceptibility interaction model. A circuit or a system is deemed to be failed once the stressor has exceeded the susceptibility limits. For on-line prediction, validated stressor v...