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)

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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.

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• Item metadata

  Item type:	Article
  ID code:	60481
  Dates:	Date Event 17 April 2017 Published 17 April 2017 Published Online 5 March 2017 Accepted
  Notes:	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:	08 Apr 2024 23:26
  URI:	https://strathprints.strath.ac.uk/id/eprint/60481