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Maximum likelihood gradient-based iterative estimation algorithm for a class of input nonlinear controlled autoregressive ARMA systems. (English) Zbl 1345.93148

Summary: This paper considers the parameter estimation problem for an input nonlinear controlled autoregressive ARMA model. The basic idea is to combine the maximum likelihood principle and the gradient search and to present a maximum likelihood gradient-based iterative estimation algorithm. The analysis and simulation results show that the proposed algorithm can effectively estimate the parameters of the input nonlinear controlled autoregressive ARMA systems.

MSC:

93E10 Estimation and detection in stochastic control theory
93C10 Nonlinear systems in control theory
60G35 Signal detection and filtering (aspects of stochastic processes)
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