Using Bootstrapping to Determine Artificial Neural Network Confidence Intervals—Case Study of Particleboard Internal Bond Determined from Production Data

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AudienciaPúblico en generales_ES
CoberturaMéxico [143]es_ES
Fecha de ingreso2026-09-09T01:06:07Z
Fecha de publicación2025-01-01
ResumenTensile strength perpendicular to the plane of the board (also known as the Internal Bond—IB), determined in accordance with standard EN 319, is one of the most critical properties in particleboard quality control. Given the need for efficient, rapid methods to assess the IB in industrial contexts, artificial neural networks (ANN) have been used as a predictive modelling tool. However, one of the main limitations of these techniques is the absence of estimates associated with the uncertainty of their predictions. The present study addresses this shortfall by applying bootstrap techniques to obtain confidence intervals using estimates generated by ANN. To achieve this, multiple models were trained and validated using experimental data taken from real production processes. The results show that the methodology proposed can be used to obtain a high level of accuracy (determination coefficient R 2 = 0.96) and a coverage probability of 93%. It also provides a robust criterion to assess conformity with standard specifications. This study concludes that adding bootstrap to ANN modelling is a very useful tool for application in industrial quality control systems, as it allows decision making based on confidence intervals rather than individual values.es_ES
Doi10.3390/app15084554es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/1529
IdiomaEspañol [65]es_ES
RelaciónFrancisco García Fernández, Paloma de Palacios, Alberto García-Iruela, Luis García Esteban. (2025). Using Bootstrapping to Determine Artificial Neural Network Confidence Intervals—Case Study of Particleboard Internal Bond Determined from Production Data. Applied Sciences. https://doi.org/10.3390/app15084554es_ES
URL relacionadohttps://www.mdpi.com/2076-3417/15/8/4554es_ES
DerechosAcceso Abiertoes_ES
LicenciaBY NC NDes_ES
FuenteApplied Sciences, () vol.? (2025)
Palabra claveANNes_ES
Palabra clavebootstrapes_ES
Palabra claveproductiones_ES
Palabra claveparticleboardes_ES
Palabra claveinternal bondes_ES
ClasificaciónCIENCIAS SOCIALES [5]:CIENCIAS ECONÓMICAS [53]:ORGANIZACIÓN Y DIRECCIÓN DE EMPRESAS [5311]es_ES
TítuloUsing Bootstrapping to Determine Artificial Neural Network Confidence Intervals—Case Study of Particleboard Internal Bond Determined from Production Dataes_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
ÁreaCIENCIAS SOCIALES [5]
ÁreaCIENCIAS SOCIALES [5]es_ES
AutorFRANCISCO GARCÍA FERNÁNDEZ;858
AutorPALOMA DE PALACIOS
AutorALBERTO GARCÍA-IRUELA
AutorLUIS GARCÍA ESTEBAN
AutorFRANCISCO GARCÍA FERNÁNDEZ;858es_ES
AutorPALOMA DE PALACIOSes_ES
AutorALBERTO GARCÍA-IRUELAes_ES
AutorLUIS GARCÍA ESTEBANes_ES
DisciplinaCIENCIAS ECONÓMICAS [53]es_ES
EdiciónPrimeraes_ES
SubdisciplinaORGANIZACIÓN Y DIRECCIÓN DE EMPRESAS [5311]es_ES
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES

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