Abstract
In the domain of industrial process modeling and control, Hammerstein model has been used widely to describe a class of nonlinear systems. Goethals et al. (2005) proposed a method based on Least Squares Support Vector Machines (LSSVM) to identify the input-output relationship of the Hammerstein model. Unfortunately, as the data points grow, this kernel learning approach costs much time correspondingly. Besides, Goethals's technique is not suitable for the on-line identification. To this end, a recursive nonlinear identification method is proposed in this paper. The basic idea is to get the recursive form of the parts of the high-dimensional matrix arisen from the optimization derivation, and get the estimation with the trick of sub-inverse matrix. With this new LSSVM approach, the Hammerstein model can be obtained recursively and much quickly, which is crucial to industrial applications that require online estimation and prediction. The simulation illustrates the validity and feasibility of the developed online identification method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
