期刊
NONLINEAR DYNAMICS
卷 95, 期 3, 页码 2079-2092出版社
SPRINGER
DOI: 10.1007/s11071-018-4677-0
关键词
Nonlinear ExpAR model; Parameter estimation; Hierarchical identification; Multi-innovation identification; Negative gradient search
资金
- 111 Project [B12018]
- National Natural Science Foundation of China [61273194]
- National First-Class Discipline Program of Light Industry Technology and Engineering [LITE2018-26]
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.
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