Data filtering based forgetting factor stochastic gradient algorithm for Hammerstein systems with saturation and preload nonlinearities. Issue 16 (November 2016) - Ma, Junxia; Xiong, Weili; Ding, Feng; Alsaedi, Ahmed; Hayat, Tasawar
Abstract: This paper considers the parameter estimation problem for Hammerstein systems with saturation and preload nonlinearities. Based on the key term separation technique, the output of the system is expressed as a linear combination of all the system parameters. By introducing the forgetting factors and using the data filtering technique, a data filtering based forgetting factor stochastic gradient (F-FF-SG) algorithm is presented. The simulation examples illustrate that the F-FF-SG algorithm has faster convergence rates and better parameter estimation accuracies than the stochastic gradient algorithm and the data filtering based stochastic gradient algorithm.