Gravity anomaly interpolation based on genetic algorithm improved back-propagation neural network

Dongming Zhao; Huan Bao; Qingbin Wang; Zhan Gao

doi:10.1117/12.904959

15 November 2011 Gravity anomaly interpolation based on genetic algorithm improved back-propagation neural network

Dongming Zhao, Huan Bao, Qingbin Wang, Zhan Gao

Proceedings Volume 8321, Seventh International Symposium on Precision Engineering Measurements and Instrumentation; 83212A (2011) https://doi.org/10.1117/12.904959
Event: Seventh International Symposium on Precision Engineering Measurements and Instrumentation, 2011, Yunnan, China

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.

Citation Download Citation

Dongming Zhao, Huan Bao, Qingbin Wang, and Zhan Gao "Gravity anomaly interpolation based on genetic algorithm improved back-propagation neural network", Proc. SPIE 8321, Seventh International Symposium on Precision Engineering Measurements and Instrumentation, 83212A (15 November 2011); https://doi.org/10.1117/12.904959

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