Genetically determined variable structure multiple model estimation

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...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2001-10, Vol.49 (10), p.2253-2261
Main Authors: Katsikas, S.K., Likothanassis, S.D., Beligiannis, G.N., Berkeris, K.G., Fotakis, D.A.
Format: Article
Language:English
Subjects:
Adaptive filters
Algorithm design and analysis
Applied sciences
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Filtering algorithms
Filtering theory
Genetic algorithms
Informatics
Information, signal and communications theory
Kalman filters
Matched filters
Partitioning algorithms
Signal and communications theory
Signal, noise
Telecommunications and information theory
Uncertainty
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Summary: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 multimodel partitioning filter (MMPF), as indicated by simulation experiments.
ISSN:1053-587X
1941-0476
DOI:10.1109/78.950781