Maximum likelihood gradient-based iterative estimation algorithm for a class of input nonlinear controlled autoregressive ARMA systems

Feiyan Chen; Feng Ding; Junhong Li

doi:10.1007/s11071-014-1712-7

Maximum likelihood gradient-based iterative estimation algorithm for a class of input nonlinear controlled autoregressive ARMA systems

Feiyan Chen, Feng Ding^*, Junhong Li

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

14 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	927-936
Number of pages	10
Journal	Nonlinear Dynamics
Volume	79
Issue number	2
DOIs	https://doi.org/10.1007/s11071-014-1712-7
Publication status	Published - Jan 2014
Externally published	Yes

Keywords

Maximum likelihood
Parameter estimation
Simulation
Stochastic gradient

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10.1007/s11071-014-1712-7

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abstract = "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.",

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AB - 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.

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KW - Simulation

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Maximum likelihood gradient-based iterative estimation algorithm for a class of input nonlinear controlled autoregressive ARMA systems

Abstract

Keywords

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