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Comparative study of neural networks and least mean square algorithm applied to the optimization of cosmetic formulations
Author(s) -
Balfagón A. C.,
SerranoHernanz A.,
Teixido J.,
TejedorEstrada R.
Publication year - 2010
Publication title -
international journal of cosmetic science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.532
H-Index - 62
eISSN - 1468-2494
pISSN - 0142-5463
DOI - 10.1111/j.1468-2494.2009.00565.x
Subject(s) - factorial experiment , fractional factorial design , artificial neural network , design of experiments , algorithm , experimental data , mathematics , set (abstract data type) , mean squared error , data set , computer science , statistics , machine learning , programming language
Synopsis In this work, a comparative study between two methods to acquire relevant information about a cosmetic formulation has been carried out. A Design of Experiments (DOE) has been applied in two stages to a capillary cosmetic cream: first, a Plackett–Burman (PB) design has been used to reduce the number of variables to be studied; second, a complete factorial design has been implemented. With the experimental data collected from the DOE, a Least Mean Square (LMS) algorithm and Artificial Neural Networks (ANN) have been utilized to obtain an equation (or model) that could explain cream viscosity. Calculations have shown that ANN are the best prediction method to fit a model to experimental data, within the interval of concentrations defined by the whole set of experiments.