Direct estimation of noisy sinusoids using abductive networks

Abdel-Aal, R.E.

doi:10.1016/j.engappai.2003.09.005

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…GMDH has been used in the classification, in determining which inputs are more important to the modeled system, and in predicting the outputs of complex systems (Barron et al, 1984). Some other usages of GMDH are in predicting tool life in drilling (Lee et al, 1999), predicting temperature distributions (Fulcher and Brown, 1994), predicting an acceptable product in a T-shape tube hydroforming process (Lin and Kwan, 2004), estimation of noisy sinusoids (Abdel-Aal, 2003), radar applications, missile guidance, multisensory signal processing, biomedical modeling, econometric forecasting, environmental systems, and many other uses (Farlow, 1984b).…”

Section: Introductionmentioning

confidence: 99%

Porosity prediction using the group method of data handling

AlBinHassan

Wang

2011

GEOPHYSICS

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The relationship between the seismic data and the reservoir properties can be modeled by using statistical approaches, such as regression and artificial neural networks (ANN); however, another nonlinear regression method, known as the group method of data handling (GMDH), has been proven to perform better than regular statistical methods. GMDH is a supervised machine learning tool that automatically self-organizes (synthesizes) the models. Although it is self-organized, like unsupervised ANN, it learns from the examples introduced similar to the supervised ANN. We apply the GMDH algorithm to seismic attributes to predict reservoir porosity. GMDH can automatically determine the best network structure, as well as the number of nodes, thus gauging sensitivity of the input without overtraining the data. Moreover, GMDH predicted porosity has better resolution than that predicted using ANN.

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