Genetically Determined Variable Structure Multiple Model Estimation

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.