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A comparative study of artificial neural network and adaptive neurofuzzy inference system for prediction of compressional wave velocity

Mansoor, Zoveidavianpour (2014) A comparative study of artificial neural network and adaptive neurofuzzy inference system for prediction of compressional wave velocity. Neural Computing & Applications, 25 (5). pp. 1169-1176. ISSN 0941-0643

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Official URL: http://dx.doi.org/10.1007/s00521-014-1604-2

Abstract

In this study, two solutions for prediction of compressional wave velocity (p wave) are presented and compared: artificial neural network (ANN) and adaptive neurofuzzy inference system (ANFIS). Series of analyses were performed to determine the optimum architecture of utilized methods using the trial and error process. Several ANNs and ANFISs are constructed, trained and validated to predict p wave in the investigated carbonate reservoir. A comparative study on prediction of p wave by ANN and ANFIS is addressed, and the quality of the target prediction was quantified in terms of the mean-squared errors (MSEs), correlation coefficient (R (2)) and prediction efficiency error. ANFIS with MSE of 0.0552 and R (2) of 0.9647, and ANN with MSE of 0.042 and R (2) of 0.976, showed better performance in comparison with MLR methods. ANN and ANFIS systems have performed comparably well and accurate for prediction of p wave.

Item Type:Article
Uncontrolled Keywords:adaptive neurofuzzy inference system, artificial neural network
Subjects:T Technology > TN Mining engineering. Metallurgy
Divisions:Petroleum and Renewable Energy Engineering
ID Code:51454
Deposited By: Siti Nor Hashidah Zakaria
Deposited On:01 Feb 2016 11:52
Last Modified:28 Jan 2019 12:30

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