Modeling of Property Prediction from Multicomponent Analytical Data Using Different Neural Networks

Majcen, Nineta; Rajer-Kanduč, Karmen; Novič, Marjana; Zupan, Jure

doi:10.1021/ac00109a039

Cited by 31 publications

(7 citation statements)

References 5 publications

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“…38,53,95 Another approach to gain insight into a complex problem is to combine the use of classical MLP (for prediction) with counter-propagation NNs to obtain contour plots of the input and output variables. 60,61…”

Section: Discussionmentioning

confidence: 99%

Neural networks in multivariate calibration

Despagne¹,

Massart²

1998

Analyst

439

270

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Section: Discussionmentioning

confidence: 99%

Neural networks in multivariate calibration

Despagne¹,

Massart²

1998

Analyst

439

270

View full text Add to dashboard Cite

“…In the present case, as in many other cases in literature, 10,16,20 this third subset is avoided and another criterion for stopping training and controlling the overfitting phenomenon is used. Neurons are added to the network until the sum-squared error falls beneath an error goal or a maximum number of neurons have been reached.…”

Section: Training the Three Rbf Networkmentioning

confidence: 96%

Radial Basis Function Networks in Liquid Chromatography: Improved Structure-Retention Relationships Compared to Principal Components Regression (Pcr) and Nonlinear Partial Least Squares Regression (Pls)

Loukas

2001

Journal of Liquid Chromatography & Related Technologies

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The application of the second most popular artificial neural networks (ANN), namely the radial basis function networks, has been developed for obtaining sufficient quantitative structureretention relationships (QSRR) with improved accuracy. The present study examined a dataset of 25 substances as solutes to two different stationary phases (silica and alumina). The solutes were analyzed to their structural descriptors and related to their retention behavior, as expressed by their capacity factors, using radial basis function (RBF) and generalized regression neural networks (GRNN) as function approximation systems.The proposed methods led to substantial gain in both the prediction ability and the computation speed of the resulting models 2239 compared to regression models. Furthermore, the results were compared with that produced from classical linear and nonlinear multivariate regression such as principal components regression (PCR) and nonlinear (polynomial) partial least squares regression (PLS). Some of the proposed ANN models diminished the number of outliers, during their implementation to unseen data (solutes), to zero.

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“…Finally, whenever dealing with cluster data, clustering or representation techniques such as PCA, Kohonen, etc., are recommended. 25,26 In our particular problem, the selection was based on the Kohonen map shown in Fig. 6.…”

Section: Training and Test Set Selectionmentioning

confidence: 99%

Application of artificial neural networks to the classification of soils from São Paulo state using near-infrared spectroscopy

Fidêncio

Ruisánchez

Poppi

2001

Analyst

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This paper describes how artificial neural networks can be used to classify multivariate data. Two types of neural networks were applied: a counter propagation neural network (CP-ANN) and a radial basis function network (RBFN). These strategies were used to classify soil samples from different geographical regions in Brazil by means of their near-infrared (diffuse reflectance) spectra. The results were better with CP-ANN (classification error 8.6%) than with RBFN (classification error 11.0%).

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Modeling of Property Prediction from Multicomponent Analytical Data Using Different Neural Networks

Abstract: Two different artificial neural network (ANN) strategies for building a model for the quantitative prediction of the property called "total color difference" are described. The models in the study are based on eight different complex oxide concentration measurements. The models obtained

Cited by 31 publications

References 5 publications

Neural networks in multivariate calibration

Neural networks in multivariate calibration

Radial Basis Function Networks in Liquid Chromatography: Improved Structure-Retention Relationships Compared to Principal Components Regression (Pcr) and Nonlinear Partial Least Squares Regression (Pls)

Application of artificial neural networks to the classification of soils from São Paulo state using near-infrared spectroscopy

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