Departmental Papers (ESE)


We consider the use of linear splines with variable knots for the approximation of unknown functions from data, motivated by control and estimation problems arising in color systems management. Unlike most popular nonlinear-in-parameters representations, piecewise linear (PL) functions can be simply inverted in a closed form. For the one-dimensional case, we present a study comparing PL and neural network (NN) approximations for several function families. Preliminary results suggest that PL, in addition to their analytical benefits, are at least competitive with NN in terms of sum square error, computational effort and training time.

Document Type

Conference Paper

Subject Area

GRASP, Kodlab

Date of this Version

September 1998


Copyright 1998 IEEE. Reprinted from Proceedings of the IEEE International Conference on Control Applications, Volume 2, 1998, pages 716-720.

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NOTE: At the time of publication, author Daniel Koditschek was affiliated with the University of Michigan. Currently, he is a faculty member in the Department of Electrical and Systems Engineering at the University of Pennsylvania.



Date Posted: 03 June 2008

This document has been peer reviewed.