International Journal of Adaptive Control and Signal Processing,
Journal Year:
2024,
Volume and Issue:
38(9), P. 3213 - 3232
Published: July 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.
International Journal of Adaptive Control and Signal Processing,
Journal Year:
2024,
Volume and Issue:
38(6), P. 2074 - 2092
Published: March 27, 2024
Summary
Nonlinear
systems
widely
exist
in
real‐word
applications
and
the
research
for
these
has
enjoyed
a
long
fruitful
history,
including
system
identification
community.
However,
modeling
nonlinear
is
often
quite
challenging
still
remains
many
unresolved
questions.
This
article
considers
online
issue
of
Hammerstein
systems,
whose
static
function
modeled
by
B‐spline
network.
First,
model
studied
constructed
using
bilinear
parameter
decomposition
model.
Second,
recursive
algorithms
are
proposed
to
find
estimates
moving
data
window
particle
swarm
optimization
procedure,
show
that
converge
their
true
values
with
low
computational
burden.
Numerical
examples
also
given
test
effectiveness
algorithms.
International Journal of Robust and Nonlinear Control,
Journal Year:
2024,
Volume and Issue:
34(11), P. 7265 - 7284
Published: March 28, 2024
Summary
In
this
paper,
we
use
the
maximum
likelihood
principle
and
negative
gradient
search
to
study
identification
issues
of
multivariate
equation‐error
systems
whose
outputs
are
contaminated
by
an
moving
average
noise
process.
The
model
decomposition
technique
is
used
decompose
system
into
several
regressive
subsystems
based
on
number
outputs.
order
improve
parameter
estimation
accuracy,
a
decomposition‐based
iterative
algorithm
proposed
means
method.
numerical
simulation
example
indicates
that
method
has
better
results
than
compared
algorithm.
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 Green Energy,
Journal Year:
2024,
Volume and Issue:
21(12), P. 2828 - 2842
Published: March 21, 2024
As
lithium-ion
batteries
are
widely
used
in
electric
vehicles,
safety
accidents
caused
by
battery
failures
emerge
one
after
another.
Nevertheless,
changes
the
internal
structure
or
characteristics
of
battery,
such
as
sudden
and
progressive
failures,
still
a
serious
problem
for
challenging
existing
fault
diagnosis
methods.
This
paper
first
performs
wavelet
packet
decomposition
on
battery's
raw
voltage
signal
to
obtain
high-quality
low-frequency
high-frequency
characteristic
components.
Then
singular
value
components
extract
corresponding
parameters,
introduces
Manhattan
average
distance
algorithm
faults.
Diagnosing
locating
faulty
units
using
Laida
criterion
(3-σ
criterion)
outlier
detection
method.
Finally,
actual
vehicle
data
were
verify
reliability,
stability,
accuracy
method,
compared
with
traditional
distance,
correlation
coefficient,
information
entropy
The
method
this
has
good
effects
vehicles
faults
vehicles.
