Application of fuzzy neural networks for predicting seismic subsidence coefficient of loess subgrade

Author

Gu, Tian-Feng ; Wang, Jia-Ding

Author_Institution

Dept. of Geol., Northwest Univ., Xi´´an, China

Volume

3

fYear

2010

Firstpage

1556

Lastpage

1559

Abstract

Taking Zhengzhou-Xi´an passenger dedicated line as an example, based on the analysis of the main influencing factors, a fuzzy neural networks model for predicting seismic subsidence coefficient of loess subgrade has been established. The model combines the fuzzy information optimization technology and neural network. It integrates the two theories, by making up the defects of the neural network in fuzzy data processing and the deficiencies of fuzzy logic in learning. The results show that model is quite suitable to predict the seismic subsidence coefficient.

Keywords

earthquake engineering; fuzzy neural nets; geophysics computing; learning (artificial intelligence); structural engineering computing; fuzzy data processing; fuzzy information optimization technology; fuzzy logic; fuzzy neural network; loess subgrade seismic subsidence coefficient prediction; Artificial neural networks; Equations; Fuzzy neural networks; Mathematical model; Predictive models; Soil; Stress; fuzzy neural networks; loess subgrade; seismic subsidence coefficient;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2010 Sixth International Conference on

Print_ISBN

978-1-4244-5958-2

Type

conf

DOI

10.1109/ICNC.2010.5583718

Filename

5583718