Evolutionary generation of fuzzy knowledge bases for diagnosing monitored railway systems

Carrascal, Alberto; Díez Oliván, Alberto; Font Fernández, José María y Manrique Gamo, Daniel (2009). Evolutionary generation of fuzzy knowledge bases for diagnosing monitored railway systems. En: "22nd International Congress on Condition monitoring and diagnostic engineering management (COMADEM 2009)", 09-11 Jun 2009, San Sebastián, España. ISBN 978-84-932064-6-8. pp. 191-198.

Descripción

Título: Evolutionary generation of fuzzy knowledge bases for diagnosing monitored railway systems
Autor/es:
  • Carrascal, Alberto
  • Díez Oliván, Alberto
  • Font Fernández, José María
  • Manrique Gamo, Daniel
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 22nd International Congress on Condition monitoring and diagnostic engineering management (COMADEM 2009)
Fechas del Evento: 09-11 Jun 2009
Lugar del Evento: San Sebastián, España
Título del Libro: Condition monitoring and diagnostic engineering management; COMADEM
Fecha: 2009
ISBN: 978-84-932064-6-8
Volumen: 22
Materias:
Palabras Clave Informales: Monitoring and diagnosis systems; Grammar guided ge netic programming; Fuzzy logic; Rule-based system; Evolutionary computation; Knowledge discovery.
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Classical approaches when building diagnosis and monitoring systems are rule-based systems, which allow the representation of existing knowledge by using rules. There are several techniques that facilitate this task, such as fuzzy logic, which allows knowledge to be modeled in an intuitive way. Nevertheless, sometimes it is not easy to define the fuzzy rule set that represents the knowledge from a certain domain. To overcome this drawback, an evolutionary system based on a grammar guided genetic programming technique for the automatic generation of fuzzy knowledge bases has been employed in diagnosing monitored railway networks. This system employs a grammar-based initialization method and both, grammar-based crossover and mutation operators, to achieve well balanced exploitation and exploration capabilities of the search space, assuring high convergence speed and low chance of getting trapped in local optima. Tests have been carried out in a real-world train monitoring domain, in which a sensor network is periodically monitoring critical train components. Results achieved show that this evolutionary system accomplishes an automatic knowledge discovery process, which is able to build a fuzzy rule base that represents the expert knowledge extracted from the domain of the detection of abnormal train conditions.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
Gobierno de EspañaEP2005-00232-C03-03Sin especificarSin especificarSin especificar

Más información

ID de Registro: 46862
Identificador DC: http://oa.upm.es/46862/
Identificador OAI: oai:oa.upm.es:46862
URL Oficial: http://www.comadem.com/
Depositado por: Memoria Investigacion
Depositado el: 04 Abr 2018 10:18
Ultima Modificación: 04 Abr 2018 10:18
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