I work a lot with adaptation methods on low cost embedded systems and
am looking for new smoothing methods for 2D learning tables. Currently
i use a method that devides a measured difference over the 4 nearest
grid points based on distance calculation, but this seems to give bad
results if the data distribution is not uniform.
Does anybody know some good methods either based on learning of grid
points or on curve fitting that can be implemented in a small
recursive form with minimum increase on system resources (both memory
and required calculation power)?

Kind Regards, Emiel Nuijten

Kind Regards, Emiel Nuijten