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Fitting the exponential autoregressive model through recursive search
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DOI：10.1016/j.jfranklin.2019.03.016.png)
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This paper focuses on the recursive parameter estimation methods for the exponential autoregressive (ExpAR) model. Applying the negative gradient search and introducing a forgetting factor, a stochastic gradient and a forgetting factor stochastic gradient algorithms are presented. In order to improve the parameter estimation accuracy and the convergence rate, the multi-innovation identification theory is employed to derive a forgetting factor multi-innovation stochastic gradient algorithm. A simulation example is provided to test the effectiveness of the proposed algorithms. (C) 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Keyword:
PARAMETER-ESTIMATION ALGORITHM
STATE-SPACE SYSTEM
IDENTIFICATION METHODS
PERFORMANCE
NOISE
DELAY
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3.7
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6.4K
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1.5W
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