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In [15], interacting multiple model is proposed for moving target location estimation in cellular network.
A particular class of multiple model, known as decoupled multiple model, is used in order to cope with the state estimation problem of nonlinear systems.
With respect to classic used multiple models, the decoupled multiple model is characterized by heterogeneous submodels in the sense that their state spaces may be of various dimensions.
Thus the decoupled multiple model is suitable for modelling complex systems with variable structure in the operating range and this fact offers promising prospects in the modelling, control and diagnosis of complex non linear systems.
This attractive kind of multiple model is characterized by submodels of which the state belong to spaces of various dimensions, in contrast to the popular Takagi-Sugeno multiple model where the dimensions of the state space of the submodels are identical.
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Additionally, one big multiple model was fitted, including all variables.
Significant variables from univariate models not being significant in the big multiple model were excluded.
The resulting multiple model was the main model with regard to this dependent variable.
All significant variables from the univariate models and the big multiple model were selected for a final model.
A multi-input multi-output (MIMO) version of second level adaptation using multiple models is developed.
An adaptive reconfigurable flight control system using the mode switching of multiple models is studied.
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