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Gradient-based iterative parameter estimation for Box-Jenkins systems. (English) Zbl 1201.94046

Summary: This paper presents a gradient-based iterative identification algorithms for Box-Jenkins systems with finite measurement input/output data. Compared with the pseudo-linear regression stochastic gradient approach, the proposed algorithm updates the parameter estimation using all the available data at each iterative computation (at each iteration), and thus can produce highly accurate parameter estimation. An example is given.

MSC:

94A13 Detection theory in information and communication theory
93E10 Estimation and detection in stochastic control theory
62M09 Non-Markovian processes: estimation
65L09 Numerical solution of inverse problems involving ordinary differential equations
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