International Journal of Adaptive Control and Signal Processing,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 21, 2025
ABSTRACT
This
paper
focuses
on
the
parameter
estimation
problem
for
pseudo‐linear
systems
with
autoregressive
moving
average
noise.
In
order
to
reduce
computational
complexity
of
identification
algorithms,
original
system
is
decomposed
into
three
submodels
and
a
three‐stage
auxiliary
model‐based
recursive
generalized
extended
least
squares
(3S‐AM‐RGELS)
algorithm
proposed
based
hierarchical
principle.
The
convergence
analysis
provided
show
that
error
can
converge
zero
under
presented
3S‐AM‐RGELS
algorithm.
Finally,
numerical
simulations
demonstrate
effectiveness
algorithms.
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.
Optimal Control Applications and Methods,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 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
Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 28, 2025
This
paper
investigates
parameters
and
states
estimation
for
a
class
of
fractional-order
state
space
systems
with
colored
noises.
To
provide
accurate
parameter
estimation,
we
suggest
novel
gradient
descent
algorithm
based
on
the
extended
Kalman
filtering.
The
new
approach
features
lower
error
variances
faster
convergence
rate
than
conventional
algorithm.
A
data
filtering
is
introduced
to
filter
input
output
data,
thereby
reducing
impact
noises
accuracy
estimates.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Фев. 19, 2025
The
non-Gaussian
characteristic
of
the
external
disturbance
poses
a
great
challenge
for
system
modeling
and
identification.
This
paper
develops
robust
recursive
estimation
algorithm
errors-in-variables
nonlinear
with
impulsive
noise.
is
formulated
by
minimizing
continuous
logarithmic
mixed
p-norm
criterion,
capable
giving
against
noise
through
an
adjustable
weight
gain.
monomials
noisy
input
are
estimated
expressions
based
on
bias
correction.
Furthermore,
hierarchical
derived
to
reduce
computational
loads.
simulation
studies
demonstrate
feasibility
proposed
algorithms.
