A hybrid neural network for seismic pattern recognition

Huang, Kou Yuan; Yang, Hsiao-Chien

doi:10.1109/ijcnn.1992.227064

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…In recent years, ANNs have been successfully used in various aspects of reservoir studies, such as geology, [2][3][4][5] geophysics, [6][7][8][9][10][11][12][13] drilling and completion, [14] formation evaluation, [15,16] production and simulation, [17,18,19] and reservoir engineering. [20][21][22][23][24][25][26][27] .…”

Section: Introductionmentioning

confidence: 99%

Obtain an Optimum Artificial Neural Network Model for Reservoir Studies

Du¹,

Weiss²,

Jian-yun³

et al. 2003

Proceedings of SPE Annual Technical Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractArtificial Neural Networks (ANNs) excel in dealing with uncertainty, fuzziness, incompleteness, and poorly defined nonlinear systems. These factors widely exist in reservoir studies. Training neural networks is a notoriously difficult problem. In training neural networks, one of the major pitfalls is overtraining, analogous to curve fitting for rule-based systems. Emperical evidence suggests that the number of records must exceed the number of neural network weights by a factor of two to minimize overtraining problems.Undertraining is another problem in which the ANNs with a simpler architecture cannot master the basic rules of input patterns. The aftermath of overtraining is much worse than that of undertraining. Based on the trial and error method, this paper first explores the overtraining problem using various defined functions and then applies the results to an oil well in the Nash Draw field in New Mexico. An optimum architecture was found for the field problem. Solutions to minimize neural network overtraining problems are presented.

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Section: Introductionmentioning

confidence: 99%

Obtain an Optimum Artificial Neural Network Model for Reservoir Studies

Du¹,

Weiss²,

Jian-yun³

et al. 2003

Proceedings of SPE Annual Technical Conference and Exhibition

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“…The weighting coefficient adjustment can be referred to in [7][8][9]. Figure 3 shows the 8-6-3 MLP with one hidden layer used in this study to predict the winning rate of the football games.…”

Section: Multilayer Perception With Back-propagation Learning Algorithmmentioning

confidence: 99%

Multilayer Perceptron for Prediction of 2006 World Cup Football Game

Huang

Chen

2011

Advances in Artificial Neural Systems

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Multilayer perceptron (MLP) with back-propagation learning rule is adopted to predict the winning rates of two teams according to their official statistical data of 2006 World Cup Football Game at the previous stages. There are training samples from three classes: win, draw, and loss. At the new stage, new training samples are selected from the previous stages and are added to the training samples, then we retrain the neural network. It is a type of on-line learning. The 8 features are selected with ad hoc choice. We use the theorem of Mirchandani and Cao to determine the number of hidden nodes. And after the testing in the learning convergence, the MLP is determined as 8-2-3 model. The learning rate and momentum coefficient are determined in the cross-learning. The prediction accuracy achieves 75% if the draw games are excluded.

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