Characterization of Mexican coffee according to mineral contents by means of multilayer perceptrons artificial neural networks

The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.

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Estimation of electrical resistivity using artificial neural networks: a case study from Lublin Basin, SE Poland

Acta Geophysica ◽

10.1007/s11600-021-00554-0 ◽

2021 ◽

Author(s):

Jakub Ważny ◽

Michał Stefaniuk ◽

Adam Cygal

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Training Data ◽

Multilayer Perceptrons ◽

Borehole Data ◽

Geological Interpretation ◽

Se Poland ◽

Geological Conditions ◽

Artificial Neural ◽

Reliable Solution

AbstractArtificial neural networks method (ANNs) is a common estimation tool used for geophysical applications. Considering borehole data, when the need arises to supplement a missing well log interval or whole logging—ANNs provide a reliable solution. Supervised training of the network on a reliable set of borehole data values with further application of this network on unknown wells allows creation of synthetic values of missing geophysical parameters, e.g., resistivity. The main assumptions for boreholes are: representation of similar geological conditions and the use of similar techniques of well data collection. In the analyzed case, a set of Multilayer Perceptrons were trained on five separate chronostratigraphic intervals of borehole, considered as training data. The task was to predict missing deep laterolog (LLD) logging in a borehole representing the same sequence of layers within the Lublin Basin area. Correlation between well logs data exceeded 0.8. Subsequently, magnetotelluric parametric soundings were modeled and inverted on both boreholes. Analysis showed that congenial Occam 1D models had better fitting of TM mode of MT data in each case. Ipso facto, synthetic LLD log could be considered as a basis for geophysical and geological interpretation. ANNs provided solution for supplementing datasets based on this analytical approach.

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Optimization of a thin-walled element geometry using a system integrating neural networks and finite element method

Archives of Metallurgy and Materials ◽

10.1515/amm-2017-0067 ◽

2017 ◽

Vol 62 (1) ◽

pp. 435-442 ◽

Cited By ~ 4

Author(s):

P. Golewski ◽

J. Gajewski ◽

T. Sadowski

Keyword(s):

Neural Networks ◽

Finite Element Method ◽

Finite Element ◽

Artificial Neural Networks ◽

Integrated System ◽

Multilayer Perceptrons ◽

Optimization Of Geometry ◽

Thin Walled ◽

Artificial Neural ◽

Element Method

Abstract Artificial neural networks [ANNs] are an effective method for predicting and classifying variables. This article presents the application of an integrated system based on artificial neural networks and calculations by the finite element method [FEM] for the optimization of geometry of a thin-walled element of an air structure. To ensure optimal structure, the structure’s geometry was modified by creating side holes and ribs, also with holes. The main criterion of optimization was to reduce the structure’s weight at the lowest possible deformation of the tested object. The numerical tests concerned a fragment of an elevator used in the “Bryza” aircraft. The tests were conducted for networks with radial basis functions [RBF] and multilayer perceptrons [MLP]. The calculations described in the paper are an attempt at testing the FEM - ANN system with respect to design optimization.

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