Adaptive filtering-based multi-innovation gradient algorithm for input nonlinear systems with autoregressive noise
Mao, Yawen and Ding, Feng and Yang, Erfu (2017) Adaptive filtering-based multi-innovation gradient algorithm for input nonlinear systems with autoregressive noise. International Journal of Adaptive Control and Signal Processing. ISSN 0890-6327 (https://doi.org/10.1002/acs.2772)
Preview |
Text.
Filename: Mao_etal_IJACSP_2017_Adaptive_filtering_based_multi_innovation_gradient_algorithm_for_input_nonlinear_systems.pdf
Accepted Author Manuscript Download (263kB)| Preview |
Abstract
In this paper, by means of the adaptive filtering technique and the multi-innovation identification theory, an adaptive filtering-based multi-innovation stochastic gradient identification algorithm is derived for Hammerstein nonlinear systems with colored noise. The new adaptive filtering configuration consists of a noise whitening filter and a parameter estimator. The simulation results show that the proposed algorithm has higher parameter estimation accuracies and faster convergence rates than the multi-innovation stochastic gradient algorithm for the same innovation length. As the innovation length increases, the filtering-based multi-innovation stochastic gradient algorithm gives smaller parameter estimation errors than the recursive least squares algorithm.
ORCID iDs
Mao, Yawen, Ding, Feng and Yang, Erfu ORCID: https://orcid.org/0000-0003-1813-5950;-
-
Item type: Article ID code: 60481 Dates: DateEvent17 April 2017Published17 April 2017Published Online5 March 2017AcceptedNotes: This is the peer reviewed version of the following article: Mao, Y., Ding, F., & Yang, E. (2017). Adaptive filtering-based multi-innovation gradient algorithm for input nonlinear systems with autoregressive noise. International Journal of Adaptive Control and Signal Processing, which has been published in final form at https://doi.org/10.1002/acs.2772. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. Subjects: Technology Department: Faculty of Engineering > Design, Manufacture and Engineering Management Depositing user: Pure Administrator Date deposited: 20 Apr 2017 10:55 Last modified: 21 Oct 2024 17:02 URI: https://strathprints.strath.ac.uk/id/eprint/60481