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Proceedings Paper

Gravity anomaly interpolation based on genetic algorithm improved back-propagation neural network
Author(s): Dongming Zhao; Huan Bao; Qingbin Wang; Zhan Gao
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Paper Abstract

The principal weakness of the traditional BP Neural Network (BP NN) is that it cannot avoid local minimum, while the Genetic Algorithm (GA) has the ability of globally optimum-searching, and therefore a new approach, GA-improved BP NN method, was presented for gravity anomaly interpolation. Firstly GA was used for optimizing the initial link weights as well as the threshold of the layers of the traditional BP NN, and then the training was completed using BP method. Numerical experiments were performed for gravity anomaly interpolation based on field measurements using BP NN and GA-improved BP NN respectively. Through comparison among the results, we found that not only the convergence rate and generalization ability of GA improved BP NN are higher than those of the traditional BP NN, but also the efficiency of the GA improved BP algorithm is more satisfactory.

Paper Details

Date Published: 16 November 2011
PDF: 6 pages
Proc. SPIE 8321, Seventh International Symposium on Precision Engineering Measurements and Instrumentation, 83212A (16 November 2011); doi: 10.1117/12.904959
Show Author Affiliations
Dongming Zhao, Zhengzhou Surveying and Mapping Institute (China)
Huan Bao, Zhengzhou Surveying and Mapping Institute (China)
Qingbin Wang, Zhengzhou Surveying and Mapping Institute (China)
Zhan Gao, Henan Institute of Engineering (China)


Published in SPIE Proceedings Vol. 8321:
Seventh International Symposium on Precision Engineering Measurements and Instrumentation
Kuang-Chao Fan; Man Song; Rong-Sheng Lu, Editor(s)

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