Active patterns for self-optimization : Schemes for the design of intelligent mechatronic systems

Self-optimizing mechatronic systems react autonomously and flexibly to changing conditions. They are capable of learning and optimize their behavior throughout their life cycle. The paradigm of self-optimization is originally inspired by the behavior of biological systems. The key to the successful...

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Detalles Bibliográficos
Autor principal: Schmidt, Andreas
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2006
Materias:
Ciencias Informáticas
Patterns
Network communications
Multiagent systems
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/24009
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-24009
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Patterns
Network communications
Multiagent systems
spellingShingle Ciencias Informáticas
Patterns
Network communications
Multiagent systems
Schmidt, Andreas
Active patterns for self-optimization : Schemes for the design of intelligent mechatronic systems
topic_facet Ciencias Informáticas
Patterns
Network communications
Multiagent systems
description Self-optimizing mechatronic systems react autonomously and flexibly to changing conditions. They are capable of learning and optimize their behavior throughout their life cycle. The paradigm of self-optimization is originally inspired by the behavior of biological systems. The key to the successful development of self-optimizing systems is a conceptual design process that precisely describes the desired system behavior. In the area of mechanical engineering, active principles based on physical effects such as friction or lever are widely used to concretize the construction structure and the behavior. The same approach can be found in the domain of software-engineering with software patterns such as the broker-pattern or the strategy pattern. However there is no appropriate design schema for the development of intelligent mechatronic systems covering the needs to fulfill the paradigm of self-optimization. This article proposes such a schema called Active Patterns for Self-Optimization. It is shown how a catalogue of active patterns can be derived from a set of four basic active patterns. This design approach is validated for a networked mechatronic system in a multiagent setting where the behavior is implemented according to a biologically inspired technique – the neuro-fuzzy learning method.
format Objeto de conferencia
Objeto de conferencia
author Schmidt, Andreas
author_facet Schmidt, Andreas
author_sort Schmidt, Andreas
title Active patterns for self-optimization : Schemes for the design of intelligent mechatronic systems
title_short Active patterns for self-optimization : Schemes for the design of intelligent mechatronic systems
title_full Active patterns for self-optimization : Schemes for the design of intelligent mechatronic systems
title_fullStr Active patterns for self-optimization : Schemes for the design of intelligent mechatronic systems
title_full_unstemmed Active patterns for self-optimization : Schemes for the design of intelligent mechatronic systems
title_sort active patterns for self-optimization : schemes for the design of intelligent mechatronic systems
publishDate 2006
url http://sedici.unlp.edu.ar/handle/10915/24009
work_keys_str_mv AT schmidtandreas activepatternsforselfoptimizationschemesforthedesignofintelligentmechatronicsystems
bdutipo_str Repositorios
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