کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1181635 | 962966 | 2008 | 5 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra](/preview/png/1181635.png)
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 93, Issue 1, 15 August 2008, Pages 58–62
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 93, Issue 1, 15 August 2008, Pages 58–62
نویسندگان
Roman M. Balabin, Ravilya Z. Safieva, Ekaterina I. Lomakina,