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Title: Discrete-Time Model Reduction of Sampled Systems Using an Enhanced Multiresolutional Dynamic Genetic Algorithm
Authors: 國立臺灣師範大學電機工程學系
C.-C. Hsu
K.-M. Tse
W.-Y. Wang
Issue Date: 10-Oct-2001
Abstract: A framework to automatically generate a reduced-order discrete-time model for the sampled system of a continuous plant preceded by a zero-order hold (ZOH) using an enhanced multi-resolution dynamic genetic algorithm (EMDGA) is proposed in this paper. Chromosomes consisting of the denominator and the numerator parameters of the reduced-order model are coded as a vector with floating-point-type components and searched by the genetic algorithm. Therefore, a stable optimal reduced-order model satisfying the error range specified can be evolutionarily obtained. Because of the use of the multi-resolution dynamic adaptation algorithm and the genetic operators, the convergence rate of the evolution process to search for an optimal reduced-order model can be expedited. Another advantage of this approach is that the reduced discrete-time model evolves based on samples taken directly from the continuous plant, instead of the exact discrete-time model, so that computation time is saved
Other Identifiers: ntnulib_tp_E0604_02_084
Appears in Collections:教師著作

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