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dc.creatorLin, Yun-Jeng
dc.descriptionDue to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to, referencing the URI of the item.en
dc.descriptionIncludes bibliographical references: p. 35-38.en
dc.description.abstractGenetic Algorithms (GA) are very different from the traditional optimization techniques. GA is a new generation of artificial intelligence and its principles mimic the behavior of the biologic genes in the natural world. Its execution is simple and it can determine solutions in a very short time. According to these characteristics, GA is a very powerful method for optimal design and system identification. In this thesis, we will apply GA to two main topics. Chapter II is about optimal designing of journal bearings with the objective of minimizing energy dissipated through the bearings and Chapters III and IV are about identifying the unknown parameters of stiffness-damping systems and rotordynamic systems.en
dc.publisherTexas A&M University
dc.rightsThis thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries in 2008. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use.en
dc.subjectmechanical engineering.en
dc.subjectMajor mechanical engineering.en
dc.titleGenetic Algorithms applications to optimization and system identificationen
dc.typeThesisen engineeringen
dc.format.digitalOriginreformatted digitalen

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