Adaptive filtering-based multi-innovation gradient algorithm for input nonlinear systems with autoregressive noise

Yawen Mao, Feng Ding, Erfu Yang

Research output: Contribution to journalArticle

11 Citations (Scopus)

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.
LanguageEnglish
Number of pages13
JournalInternational Journal of Adaptive Control and Signal Processing
Early online date17 Apr 2017
DOIs
Publication statusE-pub ahead of print - 17 Apr 2017

Fingerprint

Adaptive filtering
Nonlinear systems
Innovation
Parameter estimation
Identification (control systems)

Keywords

  • adaptive filtering
  • multi-innovation identification theory
  • nonlinear system
  • parameter estimation
  • parameter identification
  • Kalman filter
  • state estimation
  • least squares
  • Hammerstein state space model

Cite this

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title = "Adaptive filtering-based multi-innovation gradient algorithm for input nonlinear systems with autoregressive noise",
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.",
keywords = "adaptive filtering, multi-innovation identification theory, nonlinear system, parameter estimation, parameter identification, Kalman filter, state estimation, least squares, Hammerstein state space model",
author = "Yawen Mao and Feng Ding and Erfu Yang",
note = "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.",
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N2 - 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.

AB - 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.

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