A RBF Neural Network Approach for Fitting Creep Curve of Sandstone

Yun Liang Tan; Ze Zhang

doi:10.4028/www.scientific.net/AMR.171-172.274

Paper Titles

Innovativeness of Local Government and Provincial Economy Growth - A Research by Province
p.256

Ethanol Production from Corn Stover Using Soaking Pretreatment
p.261

Power System’s Reserve Margin of Coal Indicator
p.266

A Survey on the Architecture and Character of System Informatization
p.270

A RBF Neural Network Approach for Fitting Creep Curve of Sandstone
p.274

Research on Underground Coal Mine Production Process Analysis and Informatization
p.278

Function Expansion of a Chaotic Logic Unit
p.283

The Research and Implementation of Illumination Technology in the 3D GIS Visualization
p.288

Study on Relationship of Miners′ Psychological Characteristic and Safety Degree
p.295

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 171-172A RBF Neural Network Approach for Fitting Creep...

A RBF Neural Network Approach for Fitting Creep Curve of Sandstone

Abstract:

In order to quest an effective approach for predicate the rheologic deformation of sandstone based on some experimental data, an improved approaching model of RBF neural network was set up. The results show, the training time of improved RBF neural network is only about 10 percent of that of the BP neural network; the improved RBF neural network has a high predicating accuracy, the average relative predication error is only 7.9%. It has a reference value for the similar rock mechanics problem.