|Maximum Likelihood Iterative Algorithm for Hammerstein Systems with Hard Nonlinearities
Yan Pu, Yongqing Yang*, and Jing Chen
International Journal of Control, Automation, and Systems, vol. 18, no. 11, pp.2879-2889, 2020
Abstract : In this paper, we consider several iterative algorithms for Hammerstein systems with hard nonlinearities. The Hammerstein system is first simplified as a polynomial identification model through the key term separation technique, and then the parameters are estimated by using the maximum likelihood (ML) based gradient-based iterative algorithm. Furthermore, an ML least squares auxiliary variable algorithm and an ML bias compensation gradient-based iterative algorithm are developed to identify the saturation system with colored noise. Simulation results are included to illustrate the effectiveness of the proposed algorithms.
Gradient search, Hammerstein system, key term separation, maximum likelihood, saturation nonlinearity
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