IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,
Journal Year:
2023,
Volume and Issue:
42(12), P. 4948 - 4958
Published: June 20, 2023
Memristors
have
recently
demonstrated
great
promise
in
constructing
memristive
neural
networks
with
complex
dynamics.
This
article
proposes
a
Hopfield
network
three
coupling
synaptic
weights.
The
dynamical
behaviors
of
the
triple-memristor
(TM-HNN),
which
never
been
observed
previous
Hopfield-type
networks,
include
space
multistructure
chaotic
attractors
and
initial-offset
coexisting
behaviors.
Bifurcation
diagrams,
Lyapunov
exponents,
phase
portraits,
Poincaré
maps,
basins
attraction
are
used
to
reveal
examine
specific
Theoretical
analysis
numerical
simulation
show
that
number
can
be
adjusted
by
changing
control
parameters
memristors,
position
changed
switching
initial
states
memristors.
Extreme
multistability
emerges
as
result
TM-HNN's
unique
behaviors,
making
it
more
suitable
for
applications
based
on
chaos.
Moreover,
digital
hardware
platform
is
developed
well
experimentally
demonstrated.
Finally,
we
design
pseudorandom
generator
explore
potential
application
proposed
TM-HNN.
