Title :
Optimezed Rock Mass Strength Parameter via PLS-RBF Neutral Network
Author :
Ma, Sha ; Li, Bingli
Author_Institution :
North China Univ. of Water Resources & Electr. Power, Zhengzhou, China
Abstract :
The neutral network further development is restricted in the system to some extent. The 3 layers RBF neutral network has the ability that self-study and self-remember, but sometimes because of serious multi-correlation between the variables, and a few observations while many variables, there usually will result into paralyzing in study. The partial least square regression has its advantage of building the calculation model between the variables with strong multi-correlation, especially much effective on a few data and many variables. So a new and effective method-improved neutral network has been introduced. The neutral network based on the partial least square regression. The results of example show the improved method has a few calculations and high accuracy, and provide a new way for valuing the rock mass strength parameters. Its network has been applied extensively.
Keywords :
correlation methods; least squares approximations; radial basis function networks; regression analysis; rocks; PLS-RBF neutral network; multicorrelation method; optimezed rock mass strength parameter; partial least square regression; Artificial neural networks; Computational modeling; Correlation; Data models; Fitting; Rocks; RBF neutral network; partial least square regression; rock mass strength parameter;
Conference_Titel :
Information and Computing (ICIC), 2011 Fourth International Conference on
Conference_Location :
Phuket Island
Print_ISBN :
978-1-61284-688-0
DOI :
10.1109/ICIC.2011.89