Genetically determined variable structure multiple model estimation

被引：26
作者：
Katsikas, SK [1 ]
Likothanassis, SD
Beligiannis, GN
Berketis, KG
Fotakis, DA
机构：
[1] Univ Aegean, Dept Informat & Commun Syst, Karlovassi, Greece
[2] Univ Patras, Dept Comp Engn & Informat, Patras, Greece
[3] Univ Patras, UPAIRC, Artificial Intelligence Res Ctr, Patras, Greece
[4] Comp Technol Inst, GR-26110 Patras, Greece
[5] Univ Aegean, Dept Math, Karlovassi, Greece
来源：
IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2001年 / 49卷 / 10期
关键词：
genetic algorithms; Kalman filtering; multiple model estimation; variable structure estimation;
D O I：
10.1109/78.950781
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, the multimodel partitioning theory is combined with genetic algorithms to produce a new generation of multimodel partitioning filters, whose structure varies to conform to a model set being determined dynamically and on-line by using a suitably designed genetic algorithm. The proposed algorithm does not require any knowledge of the model switching law, is practically implementable, and exhibits superior performance compared with a fixed-structure MMPF, as indicated by simulation experiments.
引用
收藏
页码：2253 / 2261
页数：9
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