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Controlling Chaos in a Neural Network Based on the Phase Space Constraint
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The chaotic neural network constructed with chaotic neurons exhibits very rich dynamic
behaviors and has a nonperiodic associative memory. In the chaotic neural network,
however, it is dicult to distinguish the stored patters from others, because the states of
output of the network are in chaos. In order to apply the nonperiodic associative memory
into information search and pattern identication, etc, it is necessary to control chaos in
this chaotic neural network. In this paper, the phase space constraint method focused on
the chaotic neural network is proposed. By analyzing the orbital of the network in phase
space, we chose a part of states to be disturbed. In this way, the evolutional spaces of
the strange attractors are constrained. The computer simulation proves that the chaos
in the chaotic neural network can be controlled with above method and the network can
converge in one of its stored patterns or their reverses which has the smallest Hamming
distance with the initial state of the network. The work claries the application prospect
of the associative dynamics of the chaotic neural network.
22-24
17
2003-09
Controlling Chaos in a Neural Network Based on the Phase Space Constraint
Dynamical Systems
CAO
Zhi-tong
Zhi-tong CAO
ZHU
Ping
Ping ZHU
CHEN
Hong-ping
Hong-ping CHEN
HE
Guo-guang
Guo-guang HE
International Journal of Modern Physics B