As 
and 
are computed from their respective values at
step k-1, SCG has two parameters, namely the initial values 
and
. Their values are not critical but should respect the conditions
and 
. Empirically Møller
has shown that bigger values of 
can lead to a slower convergence.
The third parameter is the usual quantity 
(cf. standard
backpropagation).
In SNNS, it is usually the responsibility of the user to determine
when the learning process should stop. Unfortunately, the 
adaptation mechanism sometimes assigns too large values to 
when no more progress is possible. In order to avoid floating-point
exceptions, we have added a termination criterion
to SCG. The criterion is taken from the CGMs presented
in [P
88]: stop when
is a small number used to rectify the special case of converging to a
function value of exactly zero. It is set to 
.
is a tolerance depending of the floating-point precision
of your machine, and it should be set to 
, which is
usually equal to 
(simple precision) or to 
(double
precision).
To summarize, there are four non-critical parameters:
. Should satisfy 
. If 0, will
be set to 
;
. Should satisfy 
. If 0,
will be set to 
;
. See standard backpropagation. Can be set to 0 if
you don't know what to do with it;
. Depends on the floating-point precision. Should be set to
(simple precision) or to 
(double precision). If
0, will be set to 
.
Note: SCG is a batch learning method, so shuffling the patterns has no effect.