Optimization of artificial neural networks for prediction of the unit cell parameters in orthorhombic perovskites. Comparison with multiple linear regression

Kuzmanovski, Igor; Aleksovska, Slobotka

doi:10.1016/s0169-7439(03)00092-3

Cited by 32 publications

(9 citation statements)

References 20 publications

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“…Due to the difference presented between the number of validation samples, it was not possible to apply a parametric test (as F-test, 26 or paired t-test) to compare the prediction results obtained from both models. Then, the non-parametric Mann Whitney test was applied and showed that the samples provided with the PLS model, when the reference method was dichromate oxidation and samples provided with the PLS model when the reference method was dry combustion analysis come from the same population.…”

Section: Resultsmentioning

confidence: 99%

Soil Organic Carbon Determination Using NIRS: Evaluation of Dichromate Oxidation and Dry Combustion Analysis as Reference Methods in Multivariate Calibration

Beltrame

Souza

Coelho

et al. 2016

Journal of the Brazilian Chemical Society

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Dichromate oxidation and dry combustion analysis were evaluated as reference methods to determine organic carbon in Brazilian soils using near infrared spectroscopy (NIRS) as an alternative. The main objective of this study was to evaluate which of the reference methods could provide a calibration model with higher predictive ability. A total of 161 soil samples obtained from horizons in full profiles (Parque Estadual da Mata Seca, Minas Gerais State, Brazil) were used. Models were mean centered and built from partial least squares. The dichromate oxidation method presented a lower accuracy when compared to dry combustion analysis as reference for NIRS. Figures of merit such as sensitivity, analytical sensitivity, detection and quantification limits, adjust and linearity presented results very similar. A paired t-test was applied to the figures of merit results and with 95% confidence did not show significant differences between the two methods used as reference for NIRS. The non-parametric Mann Whitney test showed that the samples provided with the partial least squares (PLS) model when the reference method was the dichromate oxidation or dry combustion analysis come from the same population, indicating that the reference methods employed for multivariate calibration from NIRS provide the same results practically.

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Section: Resultsmentioning

confidence: 99%

Soil Organic Carbon Determination Using NIRS: Evaluation of Dichromate Oxidation and Dry Combustion Analysis as Reference Methods in Multivariate Calibration

Beltrame

Souza

Coelho

et al. 2016

Journal of the Brazilian Chemical Society

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“…Therefore, numerous attempts have been made to model structural parameters and properties with physical variables of the constituent elements [4][5][6][7][8][9][10][11][12][13][14]. Our interest toward analyzing relationships between crystallochemical parameters important for the structure of isomorphous/isostructural perovskites has evolved from development of linear models through nonlinear modeling based on artificial neural networks (ANN) trained to use backpropagation of error algorithm [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Examination of the influence of different variables on prediction of unit cell parameters in perovskites using counter‐propagation artificial neural networks

Kuzmanovski

Dimitrovska-Lazova

Aleksovska

2012

Journal of Chemometrics

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In this work, the unit cell parameter (a) of the series of cubic ABX 3 perovskites was modeled using counter-propagation artificial neural networks, and the influence of different input variables was examined by using algorithm for automatic adjustment of the relative importance of the variables. The input variables used in this model were the ionic radii of A, B, and X as well as the oxidation state (z) and the electronegativity (x) of the anion.The developed models have good generalization performances-good agreement between experimental and predicted values for lattice parameter. One of the important outcomes from this work is obtained from the results of the automatic adjustment of the relative importance of input variables. That is to say, this analysis gave us an insight that the most pronounced influence on the successful prediction of the unit cell parameter of the analyzed data set of cubic ABX 3 perovskites has the effective ionic radii of B-cation. In addition to this, it may be concluded that the separation of the compounds in different regions of counter-propagation artificial neural networks was predominantly influenced by the input variables with regard to the physical parameters of the anion. Copyright

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“…12 ANNs have been found to outperform regression techniques in the prediction of ceramic material properties. [13][14][15][16] Additionally, the prediction of dielectric properties of organic materials has been attempted 17 and, again, ANNs have been found to be superior.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the functional properties of ceramic materials from composition using artificial neural networks

Scott

Coveney

Kilner

et al. 2007

Journal of the European Ceramic Society

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We describe the development of artificial neural networks (ANN) for the prediction of the properties of ceramic materials. The ceramics studied here include polycrystalline, inorganic, non-metallic materials and are investigated on the basis of their dielectric and ionic properties. Dielectric materials are of interest in telecommunication applications, where they are used in tuning and filtering equipment. Ionic and mixed conductors are the subjects of a concerted effort in the search for new materials that can be incorporated into efficient, clean electrochemical devices of interest in energy production and greenhouse gas reduction applications. Multi-layer perceptron ANNs are trained using the back-propagation algorithm and utilise data obtained from the literature to learn composition-property relationships between the inputs and outputs of the system. The trained networks use compositional information to predict the relative permittivity and oxygen diffusion properties of ceramic materials. The results show that ANNs are able to produce accurate predictions of the properties of these ceramic materials, which can be used to develop materials suitable for use in telecommunication and energy production applications.

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Optimization of artificial neural networks for prediction of the unit cell parameters in orthorhombic perovskites. Comparison with multiple linear regression

Cited by 32 publications

References 20 publications

Soil Organic Carbon Determination Using NIRS: Evaluation of Dichromate Oxidation and Dry Combustion Analysis as Reference Methods in Multivariate Calibration

Soil Organic Carbon Determination Using NIRS: Evaluation of Dichromate Oxidation and Dry Combustion Analysis as Reference Methods in Multivariate Calibration

Examination of the influence of different variables on prediction of unit cell parameters in perovskites using counter‐propagation artificial neural networks

Prediction of the functional properties of ceramic materials from composition using artificial neural networks

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