RT info:eu-repo/semantics/conferenceObject
T1 A Cooperative Unsupervised Connectionist Model to Identify the Optimal Conditions of a Pneumatic Drill
A1 Corchado, Emilio
A1 Curiel Herrera, Leticia Elena
A1 Bravo Díez, Pedro Miguel
K1 Informática
K1 Computer science
K1 Ingeniería civil
K1 Civil engineering
AB A novel connectionist method to feature selection is proposed in this paper to identify the optimal conditions to perform drilling tasks. The aim is to extract information from complex high dimensional data sets. The model used is based on a family of cost functions which maximizes the likelihood of identifying a specific distribution in a data set. It employs lateral connections derived from the Rectified Gaussian Distribution to enforce a more sparse representation in each weight vector. The data investigated is obtained from the sensors allocated in a robot used to drill and build industrial warehouses. It is hoped that in classifying this data related with the strength, the water volume for refrigerating, speed and time of each sample, it will help in the search of the best conditions to perform the drilling of reinforce concrete slabs. This would produce a great saving for the company which owns the drilling robot.
PB Springer Nature
SN 978-3-540-25055-5
YR 2005
FD 2005
LK http://hdl.handle.net/10259/9356
UL http://hdl.handle.net/10259/9356
LA eng
NO Trabajo presentado en: 4th IEEE International Workshop (WSTST), realizado el 25, 26 y 27 de mayo 2005, en Muroran (Japón)
DS Repositorio Institucional de la Universidad de Burgos
RD 11-jul-2024