This paper proposes a very simple method for increasing
the algorithm speed for separating sources from PNL mixtures
or invertingWiener systems. The method is based on a
pertinent initialization of the inverse system, whose computational
cost is very low. The nonlinear part is roughly approximated
by pushing the observations ...»»»»
This paper proposes a very simple method for increasing
the algorithm speed for separating sources from PNL mixtures
or invertingWiener systems. The method is based on a
pertinent initialization of the inverse system, whose computational
cost is very low. The nonlinear part is roughly approximated
by pushing the observations to be Gaussian; this
method provides a surprisingly good approximation even
when the basic assumption is not fully satisfied. The linear
part is initialized so that outputs are decorrelated. Experiments
shows the impressive speed improvement.^^^^