Adaptive filtering-based multi-innovation gradient algorithm for input nonlinear systems with autoregressive noise
Research output: Contribution to journal › Article
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.
|Number of pages||13|
|Journal||International Journal of Adaptive Control and Signal Processing|
|Early online date||17 Apr 2017|
|State||E-pub ahead of print - 17 Apr 2017|
- adaptive filtering, multi-innovation identification theory, nonlinear system, parameter estimation, parameter identification, Kalman filter, state estimation, least squares, Hammerstein state space model
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.