Optimal Control Applications and Methods,
Journal Year:
2024,
Volume and Issue:
45(5), P. 2346 - 2363
Published: June 17, 2024
Abstract
This
article
considers
the
iterative
identification
problems
for
a
class
of
feedback
nonlinear
systems
with
moving
average
noise.
The
model
contains
both
dynamic
linear
module
and
static
module,
which
brings
challenges
to
identification.
By
utilizing
key
term
separation
technique,
unknown
parameters
from
modules
are
included
in
parameter
vector.
Furthermore,
an
auxiliary
maximum
likelihood
gradient‐based
algorithm
is
derived
estimate
parameters.
In
addition,
stochastic
gradient
as
comparison.
numerical
simulation
results
indicate
that
can
effectively
get
more
accurate
estimates
than
algorithm.
International Journal of Robust and Nonlinear Control,
Journal Year:
2023,
Volume and Issue:
33(10), P. 5510 - 5535
Published: March 30, 2023
Abstract
For
equation‐error
autoregressive
moving
average
systems,
that
is,
Box–Jenkins
this
paper
presents
a
filtered
auxiliary
model
generalized
extended
stochastic
gradient
identification
method,
multi‐innovation
recursive
least
squares
and
method
by
using
the
filtering
idea
idea.
The
proposed
methods
can
be
to
other
linear
nonlinear
multivariable
systems
with
colored
noises.
International Journal of Adaptive Control and Signal Processing,
Journal Year:
2023,
Volume and Issue:
37(7), P. 1650 - 1670
Published: April 3, 2023
Summary
This
paper
mainly
investigates
the
issue
of
parameter
identification
for
fractional‐order
input
nonlinear
output
error
autoregressive
(IN‐OEAR)
model.
In
order
to
avoid
problem
large
computation
redundant
estimation,
form
system
can
be
expressed
by
a
linear
combination
unknown
parameters
through
key
term
separation.
Through
employing
hierarchial
principle,
IN‐OEAR
model
is
decomposed
into
two
sub‐models
with
smaller
number
parameters.
On
basis
recursive
methods,
least
squares
sub‐algorithm
and
gradient
stochastic
are
proposed
estimate
fractional‐order,
respectively.
With
aim
achieving
more
accurate
estimates,
two‐stage
multi‐innovation
algorithm
means
theory.
The
numerical
simulation
results
test
effectiveness
methods.
International Journal of Robust and Nonlinear Control,
Journal Year:
2023,
Volume and Issue:
34(2), P. 1120 - 1147
Published: Oct. 17, 2023
Abstract
This
article
investigates
the
parameter
identification
problems
of
stochastic
systems
described
by
input‐nonlinear
output‐error
(IN‐OE)
model.
IN‐OE
model
consists
two
submodels,
one
is
an
input
nonlinear
and
other
a
linear
The
difficulty
in
that
information
vector
contains
unknown
variables,
which
are
noise‐free
(true)
outputs
system,
approach
taken
here
to
replace
terms
with
auxiliary
Based
on
over‐parameterization
hierarchical
principle,
gradient‐based
iterative
algorithm
least‐squares‐based
proposed
estimate
parameters
systems.
Finally,
numerical
simulation
examples
given
demonstrate
effectiveness
algorithms.
International Journal of Adaptive Control and Signal Processing,
Journal Year:
2023,
Volume and Issue:
38(1), P. 255 - 278
Published: Oct. 19, 2023
Summary
This
article
proposes
a
novel
identification
framework
for
estimating
the
parameters
of
controlled
autoregressive
moving
average
(CARARMA)
models
with
colored
noise.
By
means
building
an
auxiliary
model
and
using
hierarchical
principle,
this
investigates
highly‐efficient
parameter
estimation
algorithm.
In
order
to
meet
need
identifying
systems
large‐scale
parameters,
whole
CARARMA
system
is
separated
into
two
sets
decomposition
composition
recursive
algorithm
(i.e.,
generalized
extended
least
squares
or
decomposition‐based
algorithm)
presented.
Moreover,
analyzes
convergence
proposed
The
performance
analysis
shows
that
can
reduce
complexity
compared
without
decomposition.
International Journal of Adaptive Control and Signal Processing,
Journal Year:
2023,
Volume and Issue:
37(7), P. 1827 - 1846
Published: May 4, 2023
Summary
This
paper
addresses
the
combined
estimation
issues
of
parameters
and
states
for
fractional‐order
Hammerstein
state
space
systems
with
colored
noises.
An
extended
estimator
is
derived
by
using
parameter
estimates
to
replace
unknown
system
in
Kalman
filter.
The
hierarchical
identification
principle
introduced
solve
measurement
By
introducing
forgetting
factor,
an
filtering‐based
factor
stochastic
gradient
algorithm
presented
estimate
states,
fractional‐order.
A
numerical
example
respectively
demonstrate
feasibility
proposed
algorithm.
It
can
be
seen
that
errors
are
relatively
small,
which
reflects
algorithms
have
good
effect.
International Journal of Adaptive Control and Signal Processing,
Journal Year:
2024,
Volume and Issue:
38(4), P. 1363 - 1385
Published: Jan. 28, 2024
Summary
By
using
the
collected
batch
data
and
iterative
search,
based
on
filtering
identification
idea,
this
article
investigates
proposes
a
filtered
multi‐innovation
generalized
projection‐based
method,
gradient‐based
least
squares‐based
method
for
equation‐error
autoregressive
systems
described
by
models.
These
methods
can
be
extended
to
other
linear
nonlinear
scalar
multivariable
stochastic
with
colored
noises.
