International Journal of Robust and Nonlinear Control,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 22, 2025
ABSTRACT
This
article
focuses
on
the
parameter
estimation
issues
for
dual‐rate
Volterra
fractional‐order
autoregressive
moving
average
models.
In
case
of
sampling,
we
derive
a
identification
model
system
and
implement
intersample
output
with
help
an
auxiliary
method.
Then,
combined
self‐organizing
map
technique,
propose
Aitken
multi‐innovation
gradient‐based
iterative
algorithm.
The
parameters
are
estimated
using
algorithm,
whereas
differential
orders
determined
Moreover,
computational
cost
proposed
algorithm
is
analyzed
floating
point
operation.
Finally,
convergence
analysis
simulation
examples
show
effectiveness
Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering,
Journal Year:
2024,
Volume and Issue:
238(10), P. 1763 - 1784
Published: July 27, 2024
This
paper
mainly
discussed
the
highly
efficient
iterative
identification
methods
for
bilinear
systems
with
autoregressive
moving
average
noise.
Firstly,
input-output
representation
of
is
derived
through
eliminating
unknown
state
variables
in
model.
Then
based
on
maximum-likelihood
principle,
a
gradient-based
(ML-GI)
algorithm
proposed
to
identify
parameters
colored
noises.
For
improving
computational
efficiency,
original
model
divided
into
three
sub-identification
models
smaller
dimensions
and
fewer
parameters,
hierarchical
(H-ML-GI)
by
using
principle.
A
(GI)
given
comparison.
Finally,
algorithms
are
verified
simulation
example
practical
continuous
stirred
tank
reactor
(CSTR)
example.
The
results
show
that
effective
identifying
noises
H-ML-GI
has
higher
efficiency
faster
convergence
rate
than
ML-GI
GI
algorithm.
Algorithms,
Journal Year:
2023,
Volume and Issue:
16(5), P. 257 - 257
Published: May 17, 2023
Road
traffic
accidents
are
a
significant
public
health
issue,
accounting
for
almost
1.3
million
deaths
worldwide
annually,
with
millions
more
experiencing
non-fatal
injuries.
A
variety
of
subjective
and
objective
factors
contribute
to
the
occurrence
accidents,
making
it
difficult
predict
prevent
them
on
new
road
sections.
Artificial
neural
networks
(ANN)
have
demonstrated
their
effectiveness
in
predicting
using
limited
data
sets.
This
study
presents
two
ANN
models
common
roads
Republic
Serbia
Srpska
(Bosnia
Herzegovina)
that
can
be
easily
determined,
such
as
length,
terrain
type,
width,
average
daily
volume,
speed
limit.
The
number
well
severity
consequences,
including
fatalities,
injuries
property
damage.
developed
optimal
network
showed
good
generalization
capabilities
collected
foresee,
could
used
accurately
observed
outputs,
based
input
parameters.
highest
values
r2
ANN1
ANN2
were
0.986,
0.988,
0.977,
0.990,
0.969,
accordingly,
training,
testing
validation
cycles.
Identifying
most
influential
assist
improving
safety
reducing
accidents.
Overall,
this
research
highlights
potential
supporting
decision-making
transportation
planning.
Optimal Control Applications and Methods,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 13, 2025
ABSTRACT
This
article
addresses
the
parameter
estimation
problems
of
bilinear
output‐error
systems,
and
auxiliary
model
identification
idea
particle
filtering
technique
are
adopted
to
overcome
obstacle
resulting
from
unknown
true
outputs.
Then
a
filtering‐based
forgetting
factor
stochastic
gradient
algorithm
is
proposed
for
systems.
To
enhance
convergence
rate
accuracy
estimation,
we
expand
scalar
innovation
an
vector
develop
multi‐innovation
algorithm.
Finally,
numerical
example
practical
continuous
stirred
tank
reactor
process
provided
show
that
discussed
methods
work
well.
The
results
indicate
algorithms
effective
identifying
systems
can
generate
more
accurate
estimates
than
model‐based
International Journal of Robust and Nonlinear Control,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 22, 2025
ABSTRACT
This
article
focuses
on
the
parameter
estimation
issues
for
dual‐rate
Volterra
fractional‐order
autoregressive
moving
average
models.
In
case
of
sampling,
we
derive
a
identification
model
system
and
implement
intersample
output
with
help
an
auxiliary
method.
Then,
combined
self‐organizing
map
technique,
propose
Aitken
multi‐innovation
gradient‐based
iterative
algorithm.
The
parameters
are
estimated
using
algorithm,
whereas
differential
orders
determined
Moreover,
computational
cost
proposed
algorithm
is
analyzed
floating
point
operation.
Finally,
convergence
analysis
simulation
examples
show
effectiveness
