Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra

• Published in: Chemometrics and intelligent laboratory systems 2008-08, Vol.93 (1), p.58-62
• Main Authors: Balabin, Roman M., Safieva, Ravilya Z., Lomakina, Ekaterina I.
• Format: Article
• Language: English
• Subjects: Artificial neural network (ANN); Ethanol–gasoline fuel; Gasoline; Multilayer perceptron (MLP); Near infrared (NIR) spectroscopy; Wavelet neural network (WNN); Wavelet transform (WT)
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/j.chemolab.2008.04.003

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.