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Invertible Piecewise Linear Approximations for Color Reproduction

Proceedings of the IEEE International Conference on Control Applications, September 1998

Richard E. Groff, Daniel E. Koditschek, and Pramod P. Khargonekar
Department of Electrical Engineering and Computer Science, University of Michigan
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       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.
BibTeX entry
  author       = "Richard E. Groff and Daniel E. Koditschek and Pramod P. Khargonekar",
  title        = "Invertible Piecewise Linear Approximations for Color Reproduction",
  booktitle    = "Proceedings of the IEEE International Conference on Control Applications",
  year         = "1998",

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