Pedro Miguel Bravo

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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 connec-tionist models in conjunction with modelling systems. This particular industrial problem is defined by a data set relayed through sensors situated on(More)
This paper presents a multidisciplinary study that identifies and applies unsupervised connectionist models in conjunction with modelling systems, in order to determine optimal conditions to perform automated drilling tasks. This industrial problem is defined by a data set relayed through sensors situated on a robotic drill that is used to build industrial(More)
The purpose of this study is the optimization of drilling tasks in the construction of big auto-carrier storage warehouses. This is carried out by applying different Artificial Intelligence (AI) techniques: a cooperative unsupervised connectionist model (focused on the detection of some optimal drilling conditions) and software agents. These agents can(More)
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