Nonlinear set-membership estimation: a support vector machine approach
Keesman, K. J. and Stappers, R. (2004) Nonlinear set-membership estimation: a support vector machine approach. Journal of inverse and ill-posed problems, 12 (1). pp. 27-42. ISSN 1569-3945
To link to this article DOI: 10.1515/156939404773972752
In this paper a support vector machine (SVM) approach for characterizing the feasible parameter set (FPS) in non-linear set-membership estimation problems is presented. It iteratively solves a regression problem from which an approximation of the boundary of the FPS can be determined. To guarantee convergence to the boundary the procedure includes a no-derivative line search and for an appropriate coverage of points on the FPS boundary it is suggested to start with a sequential box pavement procedure. The SVM approach is illustrated on a simple sine and exponential model with two parameters and an agro-forestry simulation model.
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