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Abstract:
Dual estimation refers to the problem of simultaneously
estimating the state of a dynamic system and the model which gives
rise to the dynamics. Algorithms include expectation-maximization
(EM), Dual Kalman Filtering, and Joint Kalman methods. These
methods have been recently explored in the context of nonlinear
modeling, where a neural network is used as the functional form of
the unknown model. Typically, an Extended Kalman Filter (or
smoother) is used for the part of the algorithm that estimates the
clean state given the current estimated model. An EKF may also be
used as a state-space approach to estimating the weights of the
network. This paper points out the flaws in using the EKF, and
proposes an improvement based on a new approach called the
Unscented Transformation (UT). A substantial performance gain is
achieved with the same order of computational complexity as that of
the standard EKF. The approach is illustrated on several dual
estimation methods.
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