Elektronik ve Haberleşme Mühendisliği Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/260

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Author: search.filters.author.Beldek, UlasLanguage: search.filters.language.en

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  • Article
    Citation - WoS: 5
    Citation - Scopus: 9
    Strategy Creation, Decomposition and Distribution in Particle Navigation
    (Elsevier Science inc, 2007) Leblebicioglu, Kemal; Beldek, Ulas
    Strategy planning is crucial to control a group to achieve a number of tasks in a closed area full of obstacles. In this study, genetic programming has been used to evolve rule-based hierarchical structures to move the particles in a grid region to accomplish navigation tasks. Communications operations such as receiving and sending commands between particles are also provided to develop improved strategies. In order to produce more capable strategies, a task decomposition procedure is proposed. In addition, a conflict module is constructed to handle the challenging situations and conflicts such as blockage of a particle's pathway to destination by other particles. (C) 2006 Elsevier Inc. All rights reserved.
  • Conference Object
    Citation - WoS: 3
    Citation - Scopus: 5
    Local Decision Making and Decision Fusion in Hierarchical Levels
    (Springer, 2009) Leblebicioglu, Kemal; Beldek, Ulas
    Hierarchical problem solving is preferred when the problem is overwhelmingly complicated. In such a case, the problem should better be analyzed in hierarchical levels. At each level, some temporary solutions are obtained; then a suitable decision fusion technique is used to merge the temporary solutions for the next level. The hierarchical framework proposed in this study depends on reutilization or elimination of previous level local agents that together perform the decisions due to a decision-fusion technique: a performance criterion is set for local agents. The criterion checks the success of agents in their local regions. An agent satisfying this criterion is reutilized in the next level, whereas an agent not successful enough is removed from the agent pool in the next level. In place of a removed agent, a number of new local agents are developed. This framework is applied on a fault detection problem.