The
effectiveness
of
Fuzzy
Inference
Systems
(FISs)
in
manipulating
uncertainty
and
nonlinearity
makes
them
a
subject
significant
interest
for
decision-making
embedded
systems.
Accordingly,
optimizing
FIS
hardware
improves
its
performance,
efficiency,
capabilities,
leading
to
better
user
experience,
increased
productivity,
cost
savings.
To
be
compatible
with
the
limited
power
budget
most
systems,
this
paper
presents
framework
realize
ultra-low
hardware.
It
supports
optimizations
both
conventional
arithmetic
as
well
MSDF-computing
highly
consistent
MSDF-based
sensors.
In
all
processes
fuzzification,
inference,
defuzzification
are
done
on
serially
coming
data
bits.
demonstrate
efficiency
proposed
framework,
we
utilized
Matlab,
Chisel3,
Vivado
implement
it
from
high-level
descriptions
synthesis.
We
also
developed
Scala
library
Chisel3
establish
connection
between
these
tools,
bridging
gap,
facilitating
design
space
exploration
at
level.
Furthermore,
realized
an
navigation
autonomous
mobile
robots
unknown
environments.
Synthesis
results
show
superiority
output
our
suggested
terms
resource
usage
energy
consumption
compared
Matlab
HDL
code
generator
output.
Research Square (Research Square),
Год журнала:
2023,
Номер
unknown
Опубликована: Апрель 14, 2023
Abstract
Computational
methods
for
time
series
forecasting
have
always
an
edge
over
conventional
of
due
to
their
easy
implementation
and
prominent
characteristics
coping
with
large
amount
data.
Many
computational
fuzzy
(FTS)
been
developed
in
past
using
set,
intuitionistic
set
(IFS),
hesitant
(HFS)
incorporating
uncertainty,
non-determinism,
hesitation
forecasting.
Since
probabilistic
(PFS)
incorporates
both
non-probabilistic
uncertainties
simultaneously,
we
proposed
PFS
particle
swarm
optimization
(PSO)
based
method
FTS
First,
a
then
it
is
integrated
PSO
enhance
the
accuracy
forecasted
outputs.
Unlike
other
method,
used
optimize
number
partitions
length
intervals.
Three
diversified
data
enrolments
University
Alabama,
market
price
State
Bank
India
(SBI)
share
at
Bombay
stock
exchange
(BSE)
India,
death
cases
COVID-19
are
compare
performance
before
after
its
integration
terms
root
mean
square
error
(RMSE).
After
PSO,
outputs
increased
significantly
found
better
than
many
existing
methods.
Goodness
also
tested
tracking
signal
Willmott
index.
Research Square (Research Square),
Год журнала:
2023,
Номер
unknown
Опубликована: Авг. 8, 2023
Abstract
Intuitionistic
fuzzy
time
series
methods
provide
a
good
alternative
to
the
forecasting
problem.
It
is
possible
use
historical
values
of
as
well
membership
and
non-membership
obtained
for
effective
factors
in
improving
performance.
In
this
study,
high
order
single
variable
intuitionistic
reduced
model
first
introduced.
A
new
method
proposed
solution
problem
which
functional
structure
between
information
forecast
by
bagging
decision
trees
based
on
model.
method,
c-means
clustering
used
create
series.
To
simpler
with
Bagging
trees,
input
data
from
lagged
variables,
memberships,
are
subjected
dimension
reduction
principal
component
analysis.
The
performance
compared
popular
literature
ten
different
randomly
S&P500
stock
market.
According
results
analyses,
better
than
both
classical
some
shallow
deep
neural
networks.
Journal of Intelligent & Fuzzy Systems,
Год журнала:
2023,
Номер
45(5), С. 8717 - 8733
Опубликована: Сен. 5, 2023
Based
on
the
double
hierarchy
linguistic
term
sets
(DHLTS),
a
novel
forecasting
model
is
proposed
considering
both
internal
fluctuation
rules
and
external
correlation
of
different
time
series.
The
innovative
aspects
this
consist
of:
(i)
It
can
expresses
more
information,
providing
guarantees
for
improving
predictive
performance
model.
(ii)
equivalent
transformation
function
DHLTS
reduces
fuzzy
granularity
improves
prediction
accuracy.
(iii)
application
similarity
measures
extract
closest
from
historical
states
based
distance
operators
DHLTS.
In
addition,
experiments
TAIEX
impact
U.S.
stock
market
other
data
show
that
has
good
performance.
2022 International Russian Automation Conference (RusAutoCon),
Год журнала:
2023,
Номер
unknown, С. 392 - 397
Опубликована: Сен. 10, 2023
Time
series
forecasting
modeling
is
an
area
of
intensive
research
and
development.
Currently,
the
application
fuzzy
logic
to
time
models
has
received
a
lot
attention
improvement.
At
present,
intuitionistic
model
not
only
new
aspect
but
also
shows
its
outstanding
forecast
efficiency
when
considering
non-determinism.
In
this
paper,
we
propose
modified
proposed
based
on
optimization
discretization
by
optimal
ratio,
then
distribution
algorithm
used
determine
ratio.
The
results
were
compared
with
existing
methods
showed
better
forecasted
results.
The
effectiveness
of
Fuzzy
Inference
Systems
(FISs)
in
manipulating
uncertainty
and
nonlinearity
makes
them
a
subject
significant
interest
for
decision-making
embedded
systems.
Accordingly,
optimizing
FIS
hardware
improves
its
performance,
efficiency,
capabilities,
leading
to
better
user
experience,
increased
productivity,
cost
savings.
To
be
compatible
with
the
limited
power
budget
most
systems,
this
paper
presents
framework
realize
ultra-low
hardware.
It
supports
optimizations
both
conventional
arithmetic
as
well
MSDF-computing
highly
consistent
MSDF-based
sensors.
In
all
processes
fuzzification,
inference,
defuzzification
are
done
on
serially
coming
data
bits.
demonstrate
efficiency
proposed
framework,
we
utilized
Matlab,
Chisel3,
Vivado
implement
it
from
high-level
descriptions
synthesis.
We
also
developed
Scala
library
Chisel3
establish
connection
between
these
tools,
bridging
gap,
facilitating
design
space
exploration
at
level.
Furthermore,
realized
an
navigation
autonomous
mobile
robots
unknown
environments.
Synthesis
results
show
superiority
output
our
suggested
terms
resource
usage
energy
consumption
compared
Matlab
HDL
code
generator
output.
