Adjusting Dynamics of Hopfield Neural Network via Time-variant Stimulus
CoRR(2024)
摘要
As a paradigmatic model for nonlinear dynamics studies, the Hopfield Neural
Network (HNN) demonstrates a high susceptibility to external disturbances owing
to its intricate structure. This paper delves into the challenge of modulating
HNN dynamics through time-variant stimuli. The effects of adjustments using two
distinct types of time-variant stimuli, namely the Weight Matrix Stimulus (WMS)
and the State Variable Stimulus (SVS), along with a Constant Stimulus (CS) are
reported. The findings reveal that deploying four WMSs enables the HNN to
generate either a four-scroll or a coexisting two-scroll attractor. When
combined with one SVS, four WMSs can lead to the formation of an eight-scroll
or four-scroll attractor, while the integration of four WMSs and multiple SVSs
can induce grid-multi-scroll attractors. Moreover, the introduction of a CS and
an SVS can significantly disrupt the dynamic behavior of the HNN. Consequently,
suitable adjustment methods are crucial for enhancing the network's dynamics,
whereas inappropriate applications can lead to the loss of its chaotic
characteristics. To empirically validate these enhancement effects, the study
employs an FPGA hardware platform. Subsequently, an image encryption scheme is
designed to demonstrate the practical application benefits of the dynamically
adjusted HNN in secure multimedia communication. This exploration into the
dynamic modulation of HNN via time-variant stimuli offers insightful
contributions to the advancement of secure communication technologies.
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关键词
Adjustable dynamics,chaotic attractor,chaotic encryption,Hopfield neural network,secure communication,time-variant stimulus
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