2026-05-07T08:15:33Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/85622024-02-03T01:05:40Zcom_10259_6214com_10259_5086com_10259_2604com_10259_3847col_10259_6215col_10259_3848
Repositorio Institucional de la Universidad de Burgos
author
Sedano, Javier
author
Corchado, Emilio
author
Curiel Herrera, Leticia Elena
author
Villar, José Ramón
author
Bravo Díez, Pedro Miguel
2024-02-02T12:50:28Z
2024-02-02T12:50:28Z
2009
0020-7160
http://hdl.handle.net/10259/8562
10.1080/00207160802676612
1029-0265
Real-world processes may be improved through a combination of artificial intelligence and identification techniques. This work presents a multidisciplinary study that identifies and applies unsupervised connectionist models in conjunction with modelling systems. This particular industrial problem is defined by a data set relayed through sensors situated on a robotic drill used in the construction of industrial storage centres. The first step entails determination of the most relevant structures in the data set with the application of the connectionist architectures. The second step combines the results of the first one to identify a model for the optimal working conditions of the drilling robot that is based on low-order models such as black box that approximate the optimal form of the model. Finally, it is shown that the most appropriate model to control these industrial tasks is the Box–Jenkins algorithm, which calculates the function of a linear system from its input and output samples.
eng
Atribución-NoComercial 4.0 Internacional
Unsupervised learning
Exploratory projection pursuit
Black-box models
The application of a two-step AI model to an automated pneumatic drilling process
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/8562/1/Sedano-ijcm_2009.pdf
File
MD5
966a54760904108645d43aad19fe49b7
540434
application/pdf
Sedano-ijcm_2009.pdf