2026-06-21T19:13:33Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/85622024-02-03T01:05:40Zcom_10259_6214com_10259_5086com_10259_2604com_10259_3847col_10259_6215col_10259_3848

00925njm 22002777a 4500 dc Sedano, Javier author Corchado, Emilio author Curiel Herrera, Leticia Elena author Villar, José Ramón author Bravo Díez, Pedro Miguel author 2009 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. 0020-7160 http://hdl.handle.net/10259/8562 10.1080/00207160802676612 1029-0265 Unsupervised learning Exploratory projection pursuit Black-box models The application of a two-step AI model to an automated pneumatic drilling process