RT info:eu-repo/semantics/article
T1 The application of a two-step AI model to an automated pneumatic drilling process
A1 Sedano, Javier
A1 Corchado, Emilio
A1 Curiel Herrera, Leticia Elena
A1 Villar, José Ramón
A1 Bravo Díez, Pedro Miguel
K1 Unsupervised learning
K1 Exploratory projection pursuit
K1 Black-box models
K1 Informática
K1 Computer science
K1 Matemáticas
K1 Mathematics
AB 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.
PB Taylor & Francis
SN 0020-7160
YR 2009
FD 2009
LK http://hdl.handle.net/10259/8562
UL http://hdl.handle.net/10259/8562
LA eng
NO This research has been partially supported by project BU006A08 of the Junta de Castilla y León.
DS Repositorio Institucional de la Universidad de Burgos
RD 10-may-2024