Sliding Window Iterative Identification for Nonlinear Closed‐Loop Systems Based on the Maximum Likelihood Principle
International Journal of Robust and Nonlinear Control,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 1, 2024
ABSTRACT
The
parameter
estimation
problem
for
the
nonlinear
closed‐loop
systems
with
moving
average
noise
is
considered
in
this
article.
For
purpose
of
overcoming
difficulty
that
dynamic
linear
module
and
static
lead
to
identification
complexity
issues,
unknown
parameters
from
both
modules
are
included
a
vector
by
use
key
term
separation
technique.
Furthermore,
an
sliding
window
maximum
likelihood
least
squares
iterative
algorithm
gradient
derived
estimate
parameters.
numerical
simulation
indicates
efficiency
proposed
algorithms.
Язык: Английский
Integrating Differential Evolution into Gazelle Optimization for advanced global optimization and engineering applications
Computer Methods in Applied Mechanics and Engineering,
Год журнала:
2024,
Номер
434, С. 117588 - 117588
Опубликована: Ноя. 29, 2024
Язык: Английский
Nonlinear Marine Predator Algorithm for Robust Identification of Fractional Hammerstein Nonlinear Model under Impulsive Noise with Application to Heat Exchanger System
Communications in Nonlinear Science and Numerical Simulation,
Год журнала:
2025,
Номер
unknown, С. 108809 - 108809
Опубликована: Март 1, 2025
Язык: Английский
Design of quantum computing-based avain navigation optimization algorithm for parameter estimation of input nonlinear output error model with key term separation
Modern Physics Letters A,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 28, 2025
In
recent
years,
quantum
computing
has
been
applied
in
optimizing
different
challenging
systems
the
domains
of
science
and
engineering.
This
work
reflects
identification
input
nonlinear
output
error
(I-NOE)
model
by
using
Quantum-based
avian
navigation
optimizer
algorithm
(QANA).
QANA
is
an
evolutionary
method
inspired
precision
migratory
birds.
The
assessment
achieved
on
various
iterations,
population,
noise
levels.
complexity,
statistical,
convergence
investigation
with
arithmetic
optimization
(AOA),
coati
(COA),
grey
wolf
(GWO),
particle
swarm
(PSO),
synergistic
(SSOA),
velocity
pausing
(VPPSO)
approves
robustness
for
I-NOE
identification.
Язык: Английский
The Aitken Accelerated Gradient Algorithm for a Class of Dual‐Rate Volterra Nonlinear Systems Utilizing the Self‐Organizing Map Technique
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
Язык: Английский
Mathematical analysis of fractional Chlamydia pandemic model
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Дек. 28, 2024
Язык: Английский
DB-Net and DVR-Net: Optimized New Deep Learning Models for Efficient Cardiovascular Disease Prediction
Applied Sciences,
Год журнала:
2024,
Номер
14(22), С. 10516 - 10516
Опубликована: Ноя. 15, 2024
Cardiovascular
Disease
(CVD)
is
one
of
the
main
causes
death
in
recent
years.
To
overcome
challenges
faced
during
diagnosing
CVD
at
an
early
stage,
deep
learning
has
been
used.
With
advancements
technology,
clinical
practice
health
care
industry
likely
to
transform
significantly.
predict
CVD,
we
constructed
two
models:
Dense
Belief
Network
(DB-Net)
and
Deep
Vanilla
Recurrent
(DVR-Net).
Proximity
Weighted
Random
Affine
Shadow
sampling
balancing
technique
used
for
highly
imbalanced
Heart
Health
Indicator
dataset.
SHapley
Additive
exPlanations
exhibits
each
feature’s
contribution.
It
visualize
features
contribution
output
DB-Net
DVR-Net
prediction.
Furthermore,
10-Fold
Cross
Validation
performed
evaluating
proposed
models
performance.
Cross-dataset
evaluation
also
conducted
on
see
how
well
our
generalize
unseen
data.
Various
measures
are
assessment
models.
The
outperforms
all
base
by
achieving
accuracy
91%,
F1-score
precision
93%,
recall
89%,
execution
time
1883
s
30
epochs
with
batch
size
32.
beats
state-of-art
90%,
2853
Язык: Английский