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

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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.