Statistical learning problem of artificial neural network to control roofing process

Now software developed on the basis of artificial neural networks (ANN) has been actively implemented in construction companies to support decision-making in organization and management of construction processes. ANN learning is the main stage of its development. A key question for supervised learni...

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Vydané v:MATEC web of conferences Ročník 117; s. 100
Hlavní autori: Lapidus, Azariy, Makarov, Aleksandr
Médium: Journal Article Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: Les Ulis EDP Sciences 01.01.2017
Predmet:
Artificial neural networks
Confidence intervals
Construction sites
Decision making
Learning theory
Machine learning
Neural networks
Predictions
Process management
Roofing
Statistical analysis
Training
ISSN:2261-236X, 2274-7214, 2261-236X
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Popis
Shrnutí:Now software developed on the basis of artificial neural networks (ANN) has been actively implemented in construction companies to support decision-making in organization and management of construction processes. ANN learning is the main stage of its development. A key question for supervised learning is how many number of training examples we need to approximate the true relationship between network inputs and output with the desired accuracy. Also designing of ANN architecture is related to learning problem known as “curse of dimensionality”. This problem is important for the study of construction process management because of the difficulty to get training data from construction sites. In previous studies the authors have designed a 4-layer feedforward ANN with a unit model of 12-5-4-1 to approximate estimation and prediction of roofing process. This paper presented the statistical learning side of created ANN with simple-error-minimization algorithm. The sample size to efficient training and the confidence interval of network outputs defined. In conclusion the authors predicted successful ANN learning in a large construction business company within a short space of time.
Bibliografia:ObjectType-Conference Proceeding-1
SourceType-Conference Papers & Proceedings-1
content type line 21
ISSN:2261-236X
2274-7214
2261-236X
DOI:10.1051/matecconf/201711700100