Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra |
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Authors: | Roman M Balabin Ravilya Z Safieva Ekaterina I Lomakina |
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Affiliation: | aGubkin Russian State University of Oil and Gas, 119991 Moscow, Russia;bFaculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, 119992 Moscow, Russia |
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Abstract: | In this paper we have compared the abilities of two types of artificial neural networks (ANN): multilayer perceptron (MLP) and wavelet neural network (WNN) — for prediction of three gasoline properties (density, benzene content and ethanol content). Three sets of near infrared (NIR) spectra (285, 285 and 375 gasoline spectra) were used for calibration models building. Cross-validation errors and structures of optimized MLP and WNN were compared for each sample set. Four different transfer functions (Morlet wavelet and Gaussian derivative – for WNN; logistic and hyperbolic tangent – for MLP) were also compared. Wavelet neural network was found to be more effective and robust than multilayer perceptron. |
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Keywords: | Artificial neural network (ANN) Wavelet transform (WT) Multilayer perceptron (MLP) Wavelet neural network (WNN) Near infrared (NIR) spectroscopy Gasoline Ethanol– gasoline fuel |
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