학술논문

Optimezed Rock Mass Strength Parameter via PLS-RBF Neutral Network
Document Type
Conference
Author
Source
2011 Fourth International Conference on Information and Computing Information and Computing (ICIC), 2011 Fourth International Conference on. :196-199 Apr, 2011
Subject
Computing and Processing
Communication, Networking and Broadcast Technologies
Rocks
Artificial neural networks
Fitting
Data models
Computational modeling
Correlation
rock mass strength parameter
RBF neutral network
partial least square regression
Language
ISSN
2160-7443
2160-7451
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.