The application of a two-step AI model to an automated pneumatic drilling process
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...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2009 |
| País: | España |
| Institución: | Universidad de Burgos (UBU) |
| Repositorio: | Repositorio Institucional de la Universidad de Burgos (RIUBU) |
| OAI Identifier: | oai:riubu.ubu.es:10259/8562 |
| Acceso en línea: | http://hdl.handle.net/10259/8562 |
| Access Level: | acceso abierto |
| Palabra clave: | Unsupervised learning Exploratory projection pursuit Black-box models Informática Matemáticas Computer science Mathematics |
| Sumario: | 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. |
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