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A heterogeneous cyclic Hopfield neural network without self-connections
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Yunzhen ZHANG1, Chunlong ZHOU1,2, Han BAO2, Guangzhe ZHAO1, Bocheng BAO2. A heterogeneous cyclic Hopfield neural network without self-connections[J]. Journal of Zhejiang University Science A, 1998, -1(-1): .
@article{title="A heterogeneous cyclic Hopfield neural network without self-connections",
author="Yunzhen ZHANG1, Chunlong ZHOU1,2, Han BAO2, Guangzhe ZHAO1, Bocheng BAO2",
journal="Journal of Zhejiang University Science A",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2500350"
}
%0 Journal Article
%T A heterogeneous cyclic Hopfield neural network without self-connections
%A Yunzhen ZHANG1
%A Chunlong ZHOU1
%A 2
%A Han BAO2
%A Guangzhe ZHAO1
%A Bocheng BAO2
%J Journal of Zhejiang University SCIENCE A
%V -1
%N -1
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%@ 1673-565X
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2500350
TY - JOUR
T1 - A heterogeneous cyclic Hopfield neural network without self-connections
A1 - Yunzhen ZHANG1
A1 - Chunlong ZHOU1
A1 - 2
A1 - Han BAO2
A1 - Guangzhe ZHAO1
A1 - Bocheng BAO2
J0 - Journal of Zhejiang University Science A
VL - -1
IS - -1
SP -
EP -
%@ 1673-565X
Y1 - 1998
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A2500350
Abstract: We propose a three-neuron heterogeneous cyclic Hopfield neural network (het-CHNN) utilizing three different activation functions: the hyperbolic tangent, sine, and cosine functions. The network's globally uniformly ultimately boundedness is proved theoretically, and its chaotic dynamics are explored through numerical simulations and analog experiments. The numerical results demonstrate that the het-CHNN displays chaotic dynamics and multi-scroll chaotic attractors. Subsequently, the het-CHNN is implemented in an analog circuit and hardware experiments are performed to verify the previous numerical results. Notably, the het-CHNN successfully resolves the issue of the absence of chaos in a three-neuron CHNN, and currently appears to be the simplest three-neuron HNN that can generate chaos.
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