Sedimentary Microfacies Identification Using Principal Component Analysis and Neural Network Based on Genetic Algorithm

Due to the high labor cost and measurement difficulties of traditional methods in determination of sedimentary microfacies, principal component analysis (PCA) and back propagation neural network (BPNN) based on genetic algorithm (GA) are employed for the recognition system. PCA can extract the most...

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Bibliographic Details
Published in:	2011 Fourth International Symposium on Computational Intelligence and Design Vol. 1; pp. 211 - 215
Main Authors:	Junwei Mei, Shimi Peng
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-10-2011
Subjects:	Accuracy genetic algorithm Genetic algorithms identification Input variables neural network Neurons Presses Principal component analysis sedimentary microfacies Training
Online Access:	Get full text
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Summary:	Due to the high labor cost and measurement difficulties of traditional methods in determination of sedimentary microfacies, principal component analysis (PCA) and back propagation neural network (BPNN) based on genetic algorithm (GA) are employed for the recognition system. PCA can extract the most distinguishing vectors which have been removed noise in the discarded dimensions from the original data and extremely reduce the computational burden. GA can find the optimal weights and biases of the neutral network which will avoid meeting the local optimal value. The experimental results of this identification system show that PCA-GA-BPNN gives superior predictions over ordinary neutral network. More importantly, this method gets rid of the tedious activities, improves efficiency as well as maintains high recognition accuracy and also has significant potential applications in oil exploration and development field.
ISBN:	9781457710858 1457710854
DOI:	10.1109/ISCID.2011.61