Data filtering based forgetting factor stochastic gradient algorithm for Hammerstein systems with saturation and preload nonlinearities. Issue 16 (November 2016)

• Record Type: Journal Article
• Authors: Ma, Junxia; Xiong, Weili; Ding, Feng; Alsaedi, Ahmed; Hayat, Tasawar
• Is Part Of: Journal of the Franklin Institute. Volume 353:Issue 16(2016:Nov.)
• Page Start: 4280
• Page End: 4299
• Publication Date: 2016-11
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2016.07.025
• Languages: English
• ISSNs: 0016-0032

Abstract

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.