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Neuro–fuzzy modelling of spectroscopic data. Part B – Dye concentration prediction
Author(s) -
Marjoniemi Marja,
Mantysalo Esa
Publication year - 1997
Publication title -
journal of the society of dyers and colourists
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.297
H-Index - 49
eISSN - 1478-4408
pISSN - 0037-9859
DOI - 10.1111/j.1478-4408.1997.tb01870.x
Subject(s) - adaptive neuro fuzzy inference system , absorbance , artificial neural network , fuzzy inference system , biological system , component (thermodynamics) , fuzzy logic , range (aeronautics) , neuro fuzzy , computer science , analytical chemistry (journal) , mathematics , fuzzy control system , chemistry , artificial intelligence , materials science , chromatography , thermodynamics , physics , composite material , biology
An adaptive network–based fuzzy inference system, ANFIS, has been used for predicting dye concentrations using spectroscopic absorbance data in the visible region. The samples were two–component red/yellow dye solutions with a concentration range of 0–900 mg/l for the one component (red) while the concentration of the other component (yellow) was kept constant. The modelled system had two inputs (wavelength and absorbance) with the concentration values as output. Generalised bell–shaped membership functions were used for the inputs. The inference system used was a first–order Sugeno fuzzy model. The ANFIS models gave concentration prediction results with approximately the same standard error of prediction as artificial neural network (ANN) models. However, the ANFIS model building runs faster than in the case of ANN.