|
Notes:
|
The
objective
of this
research
was
to develop
a methodology
for
optimizing
multilayer-per
-
ceptron-type
neural
networks
by
evaluating
the
effects
of three
neural
architecture
parame-
ters,
namely,
number
of hidden
layers
(HL),
neurons
per
hidden
layer
(NHL),
and
activation
function
type
(AF),
on
the
sum
of squares
error
(SSE).
The
data
for
the
study
were
obtained
from
quality
parameters
(physicochemical
and
microbiological)
of milk
samples.
Architec-
tures
or combinations
were
organized
in groups
(G1,
G2,
and
G3)
generated
upon
inter-
spersing
one,
two,
and
three
layers.
Within
each
group,
the
networks
had
three
neurons
in
the
input
layer,
six
neurons
in the
output
layer,
three
to twenty-seven
NHL,
and
three
AF
(tan-sig,
log-sig,
and
linear)
types.
The
number
of architectures
was
determined
using
three
factorial-type
experimental
designs,
which
reached
63,
2 187,
and
50
049
combinations
for
G1,
G2
and
G3,
respectively.
Using
MATLAB
2015a,
a logical
sequence
was
designed
and
implemented
for
constructing,
training,
and
evaluating
multilayer-perceptro
n-type
neural
networks
using
parallel
computing
techniques.
The
results
show
that
HL
and
NHL
have
a
statistically
relevant
effect
on
SSE,
and
from
two
hidden
layers,
AF
also
has
a significant
effect;
thus,
both
AF
and
NHL
can
be
evaluated
to determine
the
optimal
combination
per
group.
Moreover,
in the
three
study
groups,
it is observed
that
there
is an
inverse
relation-
ship
between
the
number
of processors
and
the
total
optimization
time. |
