Model reduction of discrete interval systems using genetic algorithms

dc.contributor 國立臺灣師範大學電機工程學系 zh_tw C.-C. Hsu en_US T.-C. Lu en_US W.-Y. Wang en_US 2014-10-30T09:28:15Z 2014-10-30T09:28:15Z 2005-11-01 zh_TW
dc.description.abstract In this paper, an evolutionary approach is proposed to derive a reduced-order model for discretetime interval systems based on resemblance of discrete sequence energy between the original and reduced systems. With the use of the recursive algebraic algorithm and interval arithmetic manipulations, the problem to identify boundaries of the uncertain coefficients of the reduced-order model can be formulated as an optimization problem, which is subsequently solved by a proposed genetic algorithm. To demonstrate the effectiveness of the proposed approach, system performance of the reduced-order discrete interval model is validated based on time responses in comparison to existing approaches. Because of the time-consuming process that genetic algorithms generally exhibit, particularly the problem nature which demands heavy calculation of the fitness function, a parallel computation scheme is also presented to accelerate the evolution process to derive the reduced-order model. en_US
dc.identifier ntnulib_tp_E0604_01_032 zh_TW
dc.identifier.issn 1109-2777 zh_TW
dc.language en zh_TW
dc.publisher World Scientific and Engineering Academy and Society (WSEAS) en_US
dc.relation WSEAS Transactions on Systems, 4(11), 2073-2080. en_US
dc.title Model reduction of discrete interval systems using genetic algorithms en_US