Modeling a nonlinear process using the exponential autoregressive time series model
Xu, Huan and Ding, Feng and Yang, Erfu (2019) Modeling a nonlinear process using the exponential autoregressive time series model. Nonlinear Dynamics, 95 (3). pp. 2079-2092. ISSN 0924-090X (https://doi.org/10.1007/s11071-018-4677-0)
Preview |
Text.
Filename: Xu_etal_ND_2019_Modeling_a_nonlinear_process_using_the_exponential_autoregressive.pdf
Accepted Author Manuscript Download (325kB)| Preview |
Abstract
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.
ORCID iDs
Xu, Huan, Ding, Feng and Yang, Erfu
-
-
Item type: Article ID code: 73602 Dates: DateEvent28 February 2019Published6 December 2018Published Online21 November 2018AcceptedSubjects: Technology > Mechanical engineering and machinery Department: Faculty of Engineering > Design, Manufacture and Engineering Management Depositing user: Pure Administrator Date deposited: 13 Aug 2020 14:33 Last modified: 03 Feb 2025 02:09 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/73602