Application and research on SOM-PCA based RBF neural network in earthquake prediction

Status	已發表Published
	Application and research on SOM-PCA based RBF neural network in earthquake prediction
	Chen Y.; Wang Y.
	2010-12-01
Source Publication	4th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2010
Pages	315-320
Abstract	The problems of sample dispersion and information overlap of the earthquake prediction factors cause the insufficient precision when predict directly via neural network. For this problem, a Radial Basis Function (RBF) neural network prediction model algorithm based on Self-Organizing Map (SOM) neural network and Principal Components Analysis (PCA) is introduced in this article. Firstly, the earthquake prediction factors are divided in several categories by SOM neural network. And then, the dimensionality of the data in each category is descended by PCA respectively. At last, an RBF neural network whose input is the principal components of the data is trained to predict earthquake in each category. The simulation results show that, compare with the traditional prediction method via neural network directly, the proposed scheme can effectively improve the prediction accuracy.
Keyword	Earthquake magnitude Earthquake prediction Principal components analysis RBF neural network SOM neural network
URL	View the original
Language	英語English
Fulltext Access	View Full-Text via Scopus
Document Type	Conference paper
Collection	University of Macau
Affiliation	Guilin University of Electronic Technology
Recommended Citation GB/T 7714	Chen Y.,Wang Y.. Application and research on SOM-PCA based RBF neural network in earthquake prediction[C], 2010, 315-320.
APA	Chen Y.., & Wang Y. (2010). Application and research on SOM-PCA based RBF neural network in earthquake prediction. 4th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2010, 315-320.

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