A rule based fuzzy model for the prediction of petrophysical rock parameters

Finol, Jose; Guo, Yi; Jing, Xu

doi:10.1016/s0920-4105(00)00096-6

Cited by 149 publications

(50 citation statements)

References 12 publications

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“…To assess the model performances, some performance indices ( [23]) such as correlation coefficient (r), variance account for (VAF) (Equation (2)) and root mean square error (RMSE) (Equation (3) …”

Section: Resultsmentioning

confidence: 99%

Selection of Influential Microfabric Properties of Anisotropic Amphibolite Rocks on Its Uniaxial Compressive Strength (UCS): A Comprehensive Statistical Study

Ali¹,

Wu²

2014

JAMP

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Occasionally, in complex inherent characteristics of certain rocks, especially anisotropic rocks it may be difficult to measure the uniaxial compressive strength UCS. However, the use of empirical relationships to evaluate the UCS of rock can be more practical and economical. Consequently, this study carried out to predict UCS from microfabrics properties of banded amphibolite rocks using multiple regression analysis. Based on statistical results, rock microfabric parameters, which adequately represent the UCS of a given rock type have been selected. The results show that grain size, shape factor and quartz content have high significant correlation with UCS at 95% confidence level. From multiple regression model, approximately 84% of the variance of the UCS can be estimated by the linear combination of these three parameters. However, according to model performance criteria: correlation coefficient (R = 0.919), variance account for (VAF = 97%) and root mean square error (RMSE = 4.16) the study clearly indicates that the developed model is reliable to predict the UCS. Finally, this approach can be easily extended to the modeling of rock strength in the absence of adequate geological information or abundant data.

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Section: Resultsmentioning

confidence: 99%

Selection of Influential Microfabric Properties of Anisotropic Amphibolite Rocks on Its Uniaxial Compressive Strength (UCS): A Comprehensive Statistical Study

Ali¹,

Wu²

2014

JAMP

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show abstract

“…(2), and the root mean square error (RMSE), represented by Eq. (3), were also computed to check the performance of the developed models [46][47][48][49][50]. …”

Section: Performance Assessment Of Ann Modelsmentioning

confidence: 99%

Artificial neural network models for predicting soil thermal resistivity

Erzin

Rao

Singh

2008

International Journal of Thermal Sciences

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“…The basic idea of the Takagi-Sugeno model is the fact that an arbitrary complex system is a combination of mutually inter-linked subsystems 5 . Schematic representation of a Takagi-Sugeno fuzzy system is shown in Fig.…”

Section: Takagi-sugeno Fuzzy Systemmentioning

confidence: 99%

A Takagi-Sugeno Fuzzy System for the Prediction of River Stage Dynamic

Aqil

Kita

Yano

et al. 2006

JARQ

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An algorithm for real-time prediction of river stage dynamics using a Takagi-Sugeno fuzzy system is presented in this paper. The system is trained incrementally each time step and is used to predict onestep and multi-step ahead of river stages. The number of input variables that were considered in the analysis was determined using two statistical methods, i.e. autocorrelation and partial autocorrelation between the variables. Effectiveness of the identification technique was demonstrated by a simulation study on the river stage of the Cilalawi River in Indonesia. The numerical results of the Takagi-Sugeno fuzzy modeling method were compared with the results of a conventional linear regression model. Through inspection of the results it was found that the Takagi-Sugeno fuzzy approach was more accurate in predicting one-step and multi-step ahead of river stage dynamics than the conventional multiple linear regression approach. The Takagi-Sugeno fuzzy system was able to make a proper fuzzy rule from the training data set, which might be considered as one of the main drawbacks of the Takagi-Sugeno fuzzy system. Yet, more substantial improvement certainly should be pursued through further research to improve the forecast results at greater lead times.

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A rule based fuzzy model for the prediction of petrophysical rock parameters

Cited by 149 publications

References 12 publications

Selection of Influential Microfabric Properties of Anisotropic Amphibolite Rocks on Its Uniaxial Compressive Strength (UCS): A Comprehensive Statistical Study

Selection of Influential Microfabric Properties of Anisotropic Amphibolite Rocks on Its Uniaxial Compressive Strength (UCS): A Comprehensive Statistical Study

Artificial neural network models for predicting soil thermal resistivity

A Takagi-Sugeno Fuzzy System for the Prediction of River Stage Dynamic

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