The Application of Artificial Neural Networks With Small Data Sets: An Example for Analysis of Fracture Spacing in the Lisburne Formation, Northeastern Alaska

2006 ◽  
Author(s):  
Danial Kaviani ◽  
Thang Bui ◽  
Jerry L. Jensen ◽  
Catherine Hanks
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Small Data ◽  
Data Sets ◽  
Fracture Spacing ◽  
Small Data Sets ◽  
Artificial Neural

The Application of Artificial Neural Networks With Small Data Sets: An Example for Analysis of Fracture Spacing in the Lisburne Formation, Northeastern Alaska

2008 ◽  
Vol 11 (03) ◽  
pp. 598-605 ◽  
Author(s):  
Danial Kaviani ◽  
Thang Bui ◽  
Jerry L. Jensen ◽  
Catherine Hanks
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Petroleum Engineering ◽  
Small Data ◽  
Data Sets ◽  
Classification Problems ◽  
Error Performance ◽  
Data Set ◽  
Fracture Spacing ◽  
Small Data Sets

Summary Artificial neural networks (ANNs) have been used widely for prediction and classification problems. In particular, many methods for building ANNs have appeared in the last 2 decades. One of the continuing important limitations of using ANNs, however, is their poor ability to analyze small data sets because of overfitting. Several methods have been proposed in the literature to overcome this problem. On the basis of our study, we can conclude that ANNs that use radial basis functions (RBFs) can decrease the error of the prediction effectively when there is an underlying relationship between the variables. We have applied this and other methods to determine the factors controlling and related to fracture spacing in the Lisburne formation, northeastern Alaska. By comparing the RBF results with those from other ANN methods, we find that the former method gives a substantially smaller error than many of the alternative methods. For example, the errors in predicted fracture spacing for the Lisburne formation with conventional ANN methods are approximately 50 to 200% larger than those obtained with RBFs. With a method that predicts fracture spacing more accurately, we were able to identify more reliably the effects on the spacing of such factors as bed thickness, lithology, structural position, and degree of folding. By comparing performances of all the methods we tested, we observed that some methods that performed well in one test did not necessarily do as well in another test. This suggests that, while RBF can be expected to be among the best methods, there is no "best universal method" for all the cases, and testing different methods for each case is required. Nonetheless, through this study, we were able to identify several candidate methods and, thereby, narrow the work required to find a suitable ANN. In petroleum engineering and geosciences, the number of data is limited in many cases because of expense or logistical limitations (e.g., limited core, poor borehole conditions, or restricted logging suites). Thus, the methods used in this study should be attractive in many petroleum-engineering contexts in which complex, nonlinear relationships need to be modeled by use of small data sets. Introduction An ANN is "an information-processing system that has certain performance characteristics in common with biological neural networks" (Fausett 1994). On the basis of the "universal approximation theorem" with a sufficient number of hidden nodes, multilayer neural networks (Fig. 1) are able to predict any unknown function (Haykin 1999). ANNs are widely used in prediction and classification problems and have numerous applications in geosciences and petroleum engineering, including permeability prediction (Aminian et al. 2003), fluid-properties prediction (Sultan and Al-Kaabi 2002), and well-test-data analysis (Osman and Al-Marhoun 2005). Given a basic network structure, there is a wide variety of ANNs that can be produced. For example, different methods or criteria used to train the network produce ANNs that provide different predictions (e.g., the early-stopping and weight-decay methods.) Also, two or more neural networks can be combined to produce an ANN with better error performance or other qualities, giving the so-called "ensemble learning methods," a term that covers a large variety of methods, including stacked generalization and ensemble averaging. An additional problem is introduced when the data sets are small. This is a common situation in petroleum-engineering and geosciences applications, in which the cost of data or collection logistics may limit the number of measurements. In such instances, the use of ANNs can result in overfitting, where the model is fitted to the training data points but performs poorly for prediction of other points (Fig. 2). In this study, we try to identify—among myriad possibilities—a few ANNs that provide good error performance with limited sample numbers. After a brief review of various types of ANNs, we use a synthetic data set to discuss, apply, and compare the methods that have been proposed in the literature to overcome the small-data-sets problem. Finally, we apply these methods to an actual data set—fracture-spacing data from the Lisburne Group, northeastern Alaska—and evaluate the results.

scholarly journals Simulation of flood flow in a river system using artificial neural networks

2005 ◽  
Vol 9 (4) ◽  
pp. 313-321 ◽  
Author(s):  
R. R. Shrestha ◽  
S. Theobald ◽  
F. Nestmann
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Flow Simulation ◽  
Simulation Models ◽  
River System ◽  
Training Data ◽  
Data Sets ◽  
Network Training ◽  
Artificial Neural ◽  
Flood Flow

Abstract. Artificial neural networks (ANNs) provide a quick and flexible means of developing flood flow simulation models. An important criterion for the wider applicability of the ANNs is the ability to generalise the events outside the range of training data sets. With respect to flood flow simulation, the ability to extrapolate beyond the range of calibrated data sets is of crucial importance. This study explores methods for improving generalisation of the ANNs using three different flood events data sets from the Neckar River in Germany. An ANN-based model is formulated to simulate flows at certain locations in the river reach, based on the flows at upstream locations. Network training data sets consist of time series of flows from observation stations. Simulated flows from a one-dimensional hydrodynamic numerical model are integrated for network training and validation, at a river section where no measurements are available. Network structures with different activation functions are considered for improving generalisation. The training algorithm involved backpropagation with the Levenberg-Marquardt approximation. The ability of the trained networks to extrapolate is assessed using flow data beyond the range of the training data sets. The results of this study indicate that the ANN in a suitable configuration can extend forecasting capability to a certain extent beyond the range of calibrated data sets.

scholarly journals Artificial Neural Networks Application in Modal Analysis of Tires

2013 ◽  
Vol 13 (5) ◽  
pp. 273-278 ◽  
Author(s):  
P. Koštial ◽  
Z. Jančíková ◽  
D. Bakošová ◽  
J. Valíček ◽  
M. Harničárová ◽  
...  
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Modal Analysis ◽  
Speckle Pattern ◽  
Point Of View ◽  
Electronic Speckle Pattern Interferometry ◽  
Data Sets ◽  
Artificial Neural

Abstract The paper deals with the application of artificial neural networks (ANN) to tires’ own frequency (OF) prediction depending on a tire construction. Experimental data of OF were obtained by electronic speckle pattern interferometry (ESPI). A very good conformity of both experimental and predicted data sets is presented here. The presented ANN method applied to ESPI experimental data can effectively help designers to optimize dimensions of tires from the point of view of their noise.

scholarly journals Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

2017 ◽  
Vol 43 (4) ◽  
pp. 26-32 ◽  
Author(s):  
Sinan Mehmet Turp
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Data Sets ◽  
Adsorption Efficiency ◽  
Back Propagation Algorithm ◽  
Artificial Neural

AbstractThis study investigates the estimated adsorption efficiency of artificial Nickel (II) ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II) ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II) ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2), and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.

Apply Artificial Neural Networks to Estimate Stress Distribution around Holes Edge in Composites

2012 ◽  
Vol 490-495 ◽  
pp. 3105-3108
Author(s):  
Kamran Pazand ◽  
Younes Alizadeh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Composite Laminates ◽  
Composite Plate ◽  
Input Data ◽  
Data Sets ◽  
Fast Determination ◽  
Artificial Neural

The purpose of this paper is to estimate the fast determination of stress distribution around a circular hole in symmetric composite laminates under in-plane loading. For this purpose calculation of stress values in the composite plate around edge holes in different plies position for a finite number of input data sets using the Lekhnitskii expressions and code program. The resulting data would then be used to train artificial neural networks (ANN) which would be able to predict –accurately enough- those quantities throughout the composite plate body for any given input value in any position ply and fore and stress that impose.

Counting the Hidden Defects in Software Documents

2010 ◽  
pp. 519-538
Author(s):  
Frank Padberg
Keyword(s):  
Neural Networks ◽  
Training Data ◽  
Estimation Methods ◽  
Model Parameters ◽  
Small Data ◽  
Data Sets ◽  
Software Inspection ◽  
Bayesian Techniques ◽  
Standard Quality ◽  
Small Data Sets

The author uses neural networks to estimate how many defects are hidden in a software document. Input for the models are metrics that get collected when effecting a standard quality assurance technique on the document, a software inspection. For inspections, the empirical data sets typically are small. The author identifies two key ingredients for a successful application of neural networks to small data sets: Adapting the size, complexity, and input dimension of the networks to the amount of information available for training; and using Bayesian techniques instead of cross-validation for determining model parameters and selecting the final model. For inspections, the machine learning approach is highly successful and outperforms the previously existing defect estimation methods in software engineering by a factor of 4 in accuracy on the standard benchmark. The author’s approach is well applicable in other contexts that are subject to small training data sets.

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