| Journal: |
Engineering Analysis with Boundary Elements
el sevier
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Volume: |
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| Abstract: |
In this study, the nonlinear mathematical model of COVID-19 is investigated by stochastic solver using the scaled
conjugate gradient neural networks (SCGNNs). The nonlinear mathematical model of COVID-19 is represented
by coupled system of ordinary differential equations and is studied for three different cases of initial conditions
with suitable parametric values. This model is studied subject to seven class of human population N(t) and individuals
are categorized as: susceptible S(t), exposed E(t), quarantined Q(t), asymptotically diseased IA(t),
symptomatic diseased IS(t) and finally the persons removed from COVID-19 and are denoted by R(t). The stochastic
numerical
computing
SCGNNs
approach
will
be
used
to
examine
the
numerical
performance
of
nonlinear
mathematical
model
of
COVID-19.
The
stochastic
SCGNNs
approach
is
based
on
three
factors
by
using
procedure
of
verification,
sample
statistics,
testing
and
training.
For
this
purpose,
large
portion
of
data
is
considered,
i.e.,
70%,
16%,
14%
for
training,
testing
and
validation,
respectively.
The
efficiency,
reliability
and
authenticity
of
stochastic
numerical
SCGNNs
approach
are
analysed
graphically
in
terms
of
error
histograms,
mean
square
error,
correlation,
regression
and
finally
further
endorsed
by
graphical
illustrations
for
absolute
errors
in
the
range
of
10
05
07
to 10
for each scenario of the system model.
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