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Selection of regression models for predicting strength and deformability properties of rocks using GA

  • Published:2013-07 Issue:4 Volume:23 Page:495-501
  • ISSN:2095-2686
  • Container-title:International Journal of Mining Science and Technology
  • language:en
  • Short-container-title:International Journal of Mining Science and Technology

Author:

Manouchehrian Amin,Sharifzadeh Mostafa,Hamidzadeh Moghadam Rasoul,Nouri Tohid

Publisher

Elsevier BV

Subject

Geochemistry and Petrology,Energy Engineering and Power Technology,Geotechnical Engineering and Engineering Geology

Reference22 articles.

1. Fuzzy model for the prediction of unconfined compressive strength of rock samples;Alvarez Grima;Int J Rock Mech Min Sci,1999

2. Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models;Yilmaz;Int J Rock Mech Min Sci,2009

3. Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics;Manouchehrian;Int J Mining Sci Technol,2012

4. Genetic algorithms for the identification of additive and innovation outliers in time series;Baragona;Comput Stat Data Anal,2001

5. A genetic algorithm approach to find the best regression/econometric model among the candidates;Hasheminia;Appl Math Comput,2006

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