Empirical methods for artificial intelligence pdf download

Artificial intelligence. Citation Type. Has PDF. Publication Type. More Filters. A qualitative model of evolutionary algorithms. View 2 excerpts, cites background. An empirical study on the accuracy of computational effort in Genetic Programming. View 1 excerpt, cites background. State of the Art: Reproducibility in Artificial Intelligence.

View 3 excerpts, cites background and methods. Highly Influenced. View 3 excerpts, cites methods. Fuzzy logic accomplishes machine values of the models generated for the studies listed in intelligence by providing a mean for representing and the table shows a range of 0.

Fuzzy inference 5. In fuzzy logic, the truth of any time, unwanted convergence to local instead of global statement becomes a matter of a degree. FIS has been optimal solution, and large number of parameters used in rock mechanics to estimate rock properties.

Liou et al. Hybrid ; Heidari et al. Coal 28 UCS Ultrasound pulse velocity, water absorption, dry density, 52 0. The statistics of the R2 values of the models generated for the studies range from 0. This study made a compilation of empirical relations Compared to other forms of artificial intelligence for estimating UCS from other rock properties approaches, the hybrid ANNs produced higher R2 reported in the literature for the three types of rock values, indicating that they have more prediction and for cases where different rock types are mixed.

P-wave velocity 5 UCS Dry density, moisture content, 0. Bull Eng Geol logical assemblage of empirical relations that can be Environ 77 4 — used in mining engineering practice. The relationships Adebayo B, Aladejare AE Effect of rock properties on between UCS and other rock properties can be excavation-loading operation in selected quarries. In addition, the database will serve soft rock. Sage, Thousand Oaks application, especially when there is need to perform Akram M, Bakar MZA Correlation between uniaxial compressive strength and point load index for salt-range model selection and when quantifying the variability rocks.

The database will be Aladejare AE Development of Bayesian probabilistic particularly beneficial at small to medium-sized approaches for rock property characterization.

Doctoral project sites, where rock properties data are often too dissertation, City University of Hong Kong, Hong Kong Aladejare AE Evaluation of empirical estimation of sparse and there is need to estimate UCS of rock for uniaxial compressive strength of rock using measurements mine planning and design purposes. A future study can from index and physical tests.

J Rock Mech Geotech Eng. Tunn Undergr Space Technol —83 known conflicts of interest. Commons Attribution 4. Georisk Creative Commons licence, and indicate if changes were made. If material is Arabia. To view a copy of this licence, visit ence and neural networks: a comparative study. In: Rock Mech Min Sci — Proceedings of the symposium on exploration for rock Altindag R Correlation between P-wave velocity and engineering, Johannesburg, Balkema some mechanical properties for sedimentary rocks.

Int J rocks. Bull Eng Geol networks and gene expression programming. Eng Cargill JS, Shakoor A Evaluation of empirical methods Comput 32 2 — for measuring the uniaxial compressive strength of rock. Neural Comput Appl 30 11 — Ceryan N Application of support vector machines and Ashtari M, Mousavi SE, Cheshomi A, Khamechian M relevance vector machines in predicting uniaxial com- Evaluation of the single compressive strength test in esti- pressive strength of volcanic rocks.

J Afr Earth Sci mating uniaxial compressive and Brazilian tensile — strengths and elastic modulus of marlstone. In: neural networks. Appl Soft Com- criteria for rocks. J Geotech Geol Eng — and point load strength. Rock Mech Rock Eng 39 5 — compressive strength of microcrystalline limestone using Basu A, Kamran M Point load test on schistose rocks and single particles load test. J Pet Sci Eng tion of compressive and tensile strength of limestone via — genetic programming.

Expert Syst Appl 35 1—2 — Clifford ST An evaluation of the engineering properties Beiki M, Majdi A, Givshad AD Application of genetic of some Nigerian limestones as construction materials for programming to predict the uniaxial compressive strength highway pavements. Eng Geol 31 3—4 — and elastic modulus of carbonate rocks. Eng Geol artificial neural networks.

In: 25th US masses. Eng Geol 58 1 :1—23 symposium on rock mechanics Gokceoglu C, Zorlu K A fuzzy model to predict the Diamantis K, Gartzos E, Migiros G Study on uniaxial uniaxial compressive strength and the modulus of elasticity compressive strength, point load strength index, dynamic of a problematic rock. Eng Geol axial compressive strength of some clay-bearing rocks 3—4 — selected from turkey by nonlinear multivariable regression Diamantis K, Bellas S, Migiros G, Gartzos E Correlating and rule-based fuzzy models.

Int J Greece. Bull with bottom ash. Bull Eng Geol rocks. J Rock Mech Geotech Eng approach over hard computing. American Rock rowdale volcanic group core samples from Sella field. Rock Mech Rock Eng limestone. Rock Mech Rock Eng and regression models.

Geotech Geol Eng — 38 4 — Hoek E Rock mechanics laboratory testing in the context Fereidooni D Determination of the geotechnical char- of a consulting engineering organization. Rock Mech Rock Eng — failure criterion edition. ARMA for classification of rock material. Geotech Turkey. Pure Appl Geophys. In: Korkmazve S, to rock mechanics: an overview.

Measurement 4 42 — — Kahraman S, Fener M, Kozman E Predicting the com- Majdi A, Beiki M Evolving neural network using a pressive and tensile strength of rocks from indentation genetic algorithm for predicting the deformation modulus hardness index. Bull Eng Geol Environ neural network. Geol Bull Univ Peshawar 25 3 — —81 Karakus M, Tutmez B Fuzzy and multiple regression Manouchehrian A, Sharifzadeh M, Moghadam RH modelling for evaluation of intact rock strength based on Application of artificial neural networks and multivariate point load, Schmidt hammer and sonic velocity.

Rock statistics to estimate UCS using textural characteristics. Int J Rock Mech immanent in nervous activity. Pure works for the prediction of the unconfined compressive Appl Geophys — strength UCS from Equotip hardness. Arab J Geosci prediction of blast induced ground vibration using support — vector machine. Int J Rock the compressive and tensile strengths of rocks. Bull Eng Geol strength of rock materials by index tests using regression Environ 67 2 analysis and fuzzy inference system.

Neural Comput Appl 30 5 — Koncagul EC, Santi PM Predicting the unconfined Mohd BK Compressive strength of vuggy oolitic lime- compressive strength of the Breathitt shale using slake stones as a function of their porosity and sound propaga- durability, Shore hardness and rock structural properties. Eng Geol — review paper. Software Images icon An illustration of two photographs. Images Donate icon An illustration of a heart shape Donate Ellipses icon An illustration of text ellipses.

Empirical methods for artificial intelligence Item Preview. EMBED for wordpress. Want more? Advanced embedding details, examples, and help! Empirical Research -- 2. Exploratory Data Analysis -- 3. Basic Issues in Experiment Design -- 4.