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Basis-function optimization for subspace-based nonlinear identification of systems with measured-input nonlinearities
2004
Proceedings of the 2004 American Control Conference
unpublished
Preserved Fulltext
For nonlinear systems with measured-input nonlinearities, a subspace identification algorithm is used to identify the linear dynamics with the nonlinear mappings represented as a linear combination of basis functions. A selectiverefinement technique and a quasi-Newton optimization algorithm are used to iteratively improve the representation of the system nonlinearity. For both methods, polynomials, splines, sigmoids, wavelets, sines and cosines, or radial basis functions can be used as basis
doi:10.23919/acc.2004.1384070
fatcat:wzsuo4wkxfh7bdhnjsz5lj5yuq