A practical guide to neural nets
A practical guide to neural nets
A computer-controlled rotating polarizer stage for the petrographic microscope
Computers & Geosciences
Edge detection in petrographic images using the rotating polarizer stage
Computers & Geosciences
Artificial Intelligence: A Guide to Intelligent Systems
Artificial Intelligence: A Guide to Intelligent Systems
Advances in Feedforward Neural Networks: Demystifying Knowledge Acquiring Black Boxes
IEEE Transactions on Knowledge and Data Engineering
A novel approach for ANFIS modelling based on full factorial design
Applied Soft Computing
Expert Systems with Applications: An International Journal
Estimation of elastic constant of rocks using an ANFIS approach
Applied Soft Computing
Hi-index | 12.05 |
The uniaxial compressive strength (UCS) of rocks is an important intact rock parameter, and it is commonly used for various engineering applications. This parameter is mainly controlled by the mineralogical and textural characteristics of rocks. In this study, a soft computing method, an adaptive neuro-fuzzy inference system (ANFIS), was employed to estimate UCS from the mineral contents of certain granitic rocks selected from Turkey; nonlinear multiple regression analysis was then employed to validate these estimations. Five nonlinear multiple regressions and ANFIS models were constructed with three inputs: quartz, orthoclase and plagioclase. To determine the optimal model, various performance indices (R, values account for and root mean square error) were determined, and the model obtained from dataset #3 was selected as the optimal model. The coefficients of correlation for the nonlinear multiple regression and ANFIS models were 0.87 and 0.91, respectively. Thus, both models yielded acceptable results, and the ANFIS is a suitable method for estimating the UCS of rocks.