- Trial 1:
we learned, tested, and applied the nonlinear mapping (MLP) to the
model and observed SST datasets.
- Trial 2:
we wavelet transformed the model and observed SST datasets
into the sets
Im = Wm(1) + Wm(2) + Rm
Io = Wo(1) + Wo(2) +
where W(1) is wavelet resolution scale 1, and R is the
residual (or background).
- Trial 2 (cont.): we then learned, tested and applied the nonlinear
mapping to the datasets
Wm(1) and Wo(1)
Wm(2) and Wo(2)
Rm and Ro
and combined the results.
- In Trial 2, we looked in particular at data filtering, through
suppressing one or more resolution levels.
- Trial 3:
mapping of Im onto Ro,
and the mapping of Im onto Wo(2) +
Ro. The best result is given by the first of these Trial 3