Cooperative Learning Model based on Multi-Agent Architecture for Embedded Intelligent Systems

Villaverde San José, Mónica; Pérez Daza, David y Moreno González, Félix Antonio (2014). Cooperative Learning Model based on Multi-Agent Architecture for Embedded Intelligent Systems. En: "IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society", 30 oct - 01 nov 2014, Dallas (USA). ISBN 978-1-4799-4032-5. pp. 2742-2730.

Descripción

Título: Cooperative Learning Model based on Multi-Agent Architecture for Embedded Intelligent Systems
Autor/es:
  • Villaverde San José, Mónica
  • Pérez Daza, David
  • Moreno González, Félix Antonio
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society
Fechas del Evento: 30 oct - 01 nov 2014
Lugar del Evento: Dallas (USA)
Título del Libro: Proceedings IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society
Fecha: 2014
ISBN: 978-1-4799-4032-5
Materias:
Palabras Clave Informales: embedded artificial intelligence; learning systems; cooperative systems; intelligent agents; adaptive systems; weighting procedures; decision making
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Cooperative systems are suitable for many types of applications and nowadays these system are vastly used to improve a previously defined system or to coordinate multiple devices working together. This paper provides an alternative to improve the reliability of a previous intelligent identification system. The proposed approach implements a cooperative model based on multi-agent architecture. This new system is composed of several radar-based systems which identify a detected object and transmit its own partial result by implementing several agents and by using a wireless network to transfer data. The proposed topology is a centralized architecture where the coordinator device is in charge of providing the final identification result depending on the group behavior. In order to find the final outcome, three different mechanisms are introduced. The simplest one is based on majority voting whereas the others use two different weighting voting procedures, both providing the system with learning capabilities. Using an appropriate network configuration, the success rate can be improved from the initial 80% up to more than 90%.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
Gobierno de EspañaITP-2011- 1977-920000INNPACTO-2011Sin especificarSin especificar

Más información

ID de Registro: 37018
Identificador DC: http://oa.upm.es/37018/
Identificador OAI: oai:oa.upm.es:37018
URL Oficial: http://ieeexplore.ieee.org/document/7048892/
Depositado por: Memoria Investigacion
Depositado el: 10 Mar 2016 17:20
Ultima Modificación: 23 Feb 2017 17:34
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