International Journal of Robust and Nonlinear Control,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 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
International Journal of Adaptive Control and Signal Processing,
Год журнала:
2024,
Номер
38(10), С. 3268 - 3289
Опубликована: Июль 29, 2024
Summary
This
paper
deals
with
the
problem
of
parameter
estimation
for
feedback
nonlinear
output‐error
systems.
The
auxiliary
model‐based
recursive
least
squares
algorithm
and
stochastic
gradient
are
derived
estimation.
Based
on
process
theory,
convergence
proposed
algorithms
proved.
simulation
results
indicate
that
can
estimate
parameters
systems
effectively.
Optimal Control Applications and Methods,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 29, 2025
ABSTRACT
Through
dividing
a
multivariable
system
into
several
subsystems,
this
paper
derives
the
sub‐identification
model.
Utilizing
obtained
model,
an
auxiliary
model‐based
maximum
likelihood
forgetting
gradient
algorithm
is
derived.
Considering
enhancing
parameter
estimation
accuracy,
multi‐innovation
(AM‐ML‐MIFG)
proposed
taking
advantage
of
identification
theory.
Simulation
results
test
effectiveness
algorithms,
and
confirm
that
AM‐ML‐MIFG
has
satisfactory
performance
in
capturing
dynamic
properties
system.
International Journal of Adaptive Control and Signal Processing,
Год журнала:
2024,
Номер
38(9), С. 3213 - 3232
Опубликована: Июль 3, 2024
Summary
In
industrial
process
control
systems,
parameter
estimation
is
crucial
for
controller
design
and
model
analysis.
This
article
examines
the
issue
of
identifying
parameters
in
continuous‐time
models.
presents
a
stochastic
gradient
algorithm
recursive
least
squares
continuous
systems.
It
derives
identification
linear
systems
based
on
Laplace
transforms
input
output
To
prove
that
techniques
given
here
work,
we
have
included
simulated
example.
