19 September 2014 Baseline estimation in flame's spectra by using neural networks and robust statistics
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Abstract
This work presents a baseline estimation method in flame spectra based on artificial intelligence structure as a neural network, combining robust statistics with multivariate analysis to automatically discriminate measured wavelengths belonging to continuous feature for model adaptation, surpassing restriction of measuring target baseline for training. The main contributions of this paper are: to analyze a flame spectra database computing Jolliffe statistics from Principal Components Analysis detecting wavelengths not correlated with most of the measured data corresponding to baseline; to systematically determine the optimal number of neurons in hidden layers based on Akaike's Final Prediction Error; to estimate baseline in full wavelength range sampling measured spectra; and to train an artificial intelligence structure as a Neural Network which allows to generalize the relation between measured and baseline spectra. The main application of our research is to compute total radiation with baseline information, allowing to diagnose combustion process state for optimization in early stages.
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Hugo Garces, Luis Arias, Alejandro Rojas, "Baseline estimation in flame's spectra by using neural networks and robust statistics", Proc. SPIE 9216, Optics and Photonics for Information Processing VIII, 92160L (19 September 2014); doi: 10.1117/12.2060693; https://doi.org/10.1117/12.2060693
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