English  |  正體中文  |  简体中文  |  Items with full text/Total items : 51258/86283 (59%)
Visitors : 8011204      Online Users : 57
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/42052


    Title: Minimum-phase criterion on sampling time for sampled-data interval systems using genetic algorithms
    Authors: 許陳鑑;Hsu, Chen-chien;Lu, Tsung-chi
    Contributors: 淡江大學電機工程學系
    Keywords: Minimum-phase;Genetic algorithms;Uncertain systems;Interval plant;Sampled-data systems;Discretization;Parallel computation
    Date: 2008-09
    Issue Date: 2010-02-23 14:48:06 (UTC+8)
    Publisher: Elsevier
    Abstract: In this paper, a genetic algorithm-based approach is proposed to determine a desired sampling-time range which guarantees minimum phase behaviour for the sampled-data system of an interval plant preceded by a zero-order hold (ZOH). Based on a worst-case analysis, the identification problem of the sampling-time range is first formulated as an optimization problem, which is subsequently solved under a GA-based framework incorporating two genetic algorithms. The first genetic algorithm searches both the uncertain plant parameters and sampling time to dynamically reduce the search range for locating the desired sampling-time boundaries based on verification results from the second genetic algorithm. As a result, the desired sampling-time range ensuring minimum phase behaviour of the sampled-data interval system can be evolutionarily obtained. Because of the time-consuming process that genetic algorithms generally exhibit, particularly the problem nature which requires undertaking a large number of evolution cycles, parallel computation for the proposed genetic algorithm is therefore proposed to accelerate the derivation process. Illustrated examples in this paper have demonstrated that the proposed GA-based approach is capable of accurately locating the boundaries of the desired sampling-time range.
    Relation: Applied Soft Computing 8(4), pp.1670-1679
    DOI: 10.1016/j.asoc.2008.01.009
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

    Files in This Item:

    File SizeFormat
    0KbUnknown183View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback