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Modeling a nonlinear process using the exponential autoregressive time series model
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DOI：10.1007/s11071-018-4677-0.png)
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摘要
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The parameter estimation methods for the nonlinear exponential autoregressive (ExpAR) model are investigated in this work. Combining the hierarchical identification principle with the negative gradient search, we derive a hierarchical stochastic gradient algorithm. Inspired by the multi-innovation identification theory, we develop a hierarchical-based multi-innovation identification algorithm for the ExpAR model. Introducing two forgetting factors, a variant of the hierarchical-based multi-innovation identification algorithm is proposed. Moreover, to compare and demonstrate the serviceability of these algorithms, a nonlinear ExpAR process is taken as an example in the simulation.
Keyword:
Nonlinear ExpAR model
Parameter estimation
Hierarchical identification
Multi-innovation identification
Negative gradient search
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IF:
6
论文数:
1.4W
被引数:
4.1W
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Identification for Hammerstein nonlinear ARMAX systems based on multi-innovation fractional order stochastic gradient.png)
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