Title :
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
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;
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Print_ISBN :
978-1-4244-5958-2
DOI :
10.1109/ICNC.2010.5583718
