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Gradient-based parameter estimation for input nonlinear systems with ARMA noises based on the auxiliary model

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Abstract

This paper presents a gradient-based iterative identification algorithm and an auxiliary-model-based multi-innovation generalized extended stochastic gradient algorithm for input nonlinear systems with autoregressive moving average (ARMA) noises, i.e., the input nonlinear Box–Jenkins (IN–BJ) systems. The estimation errors given by the gradient-based iterative algorithm are smaller than the generalized extended stochastic gradient algorithm under same data lengths. A simulation example is provided.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China and by the 111 Project (B12018).

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Correspondence to Jing Chen.

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Chen, J., Zhang, Y. & Ding, R. Gradient-based parameter estimation for input nonlinear systems with ARMA noises based on the auxiliary model. Nonlinear Dyn 72, 865–871 (2013). https://doi.org/10.1007/s11071-013-0758-2

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  • DOI: https://doi.org/10.1007/s11071-013-0758-2

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