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A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks

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Title A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks
 
Creator SINGH, R
VISHAL, V
SINGH, TN
RANJITH, PG
 
Subject GRNN
Conjugate gradient descent algorithms
ANFIS
Seismic waves
UCS
IDENTIFICATION
MODEL
PARAMETERS
VELOCITY
MODULUS
 
Description The engineering properties of rocks play a significant role in planning and designing of mining and civil engineering projects. A laboratory database of mechanical and engineering properties of rocks is always required for site characterization and mineral exploitation. Due to discontinuous and variable nature of rock masses, it is difficult to obtain all physicomechanical properties of rocks precisely. Prediction of unconfined compressive strength from seismic wave velocities (Compressional wave, Shear wave) and density of rock using generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference systems (ANFIS) can be appropriate and alternate methods to minimize the time and cost of tests. GRNN and ANFIS models were trained with 41 data sets using conjugate gradient descent algorithms and hybrid learning algorithm, respectively. Performance of both the models was examined with 15 testing data sets. In the present study, obtained network performance indices such as correlation coefficient, mean absolute percentage error, root mean square error and variance account for indicate high performance of predictive capability of GRNN system and closer to actual data over the ANFIS.
 
Publisher SPRINGER
 
Date 2014-10-15T08:00:19Z
2014-10-15T08:00:19Z
2013
 
Type Article
 
Identifier NEURAL COMPUTING & APPLICATIONS, 23(2)499-506
http://dx.doi.org/10.1007/s00521-012-0944-z
http://dspace.library.iitb.ac.in/jspui/handle/100/14628
 
Language en