Least Squares based Iterative Parameter Estimation Algorithm for Stochastic Dynamical Systems with ARMA Noise Using the Model Equivalence

By means of the model equivalence theory, this paper proposes a model equivalence based least squares iterative algorithm for estimating the parameters of stochastic dynamical systems with ARMA noise. The proposed algorithm reduces the number of the unknown noise terms in the information vector and can give more accurate parameter estimates compared with the generalized extended least squares algorithm. The validity of the proposed method is evaluated through a numerical example.

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