He Guoguang, Cao Zhitong, Zhu Ping, Ogura Hisakazu
Department of Physics, Zhejiang University, 310028 Hangzhou, People's Republic of China.
Neural Netw. 2003 Oct;16(8):1195-200. doi: 10.1016/S0893-6080(03)00055-8.
The chaotic neural network constructed with chaotic neuron shows the associative memory function, but its memory searching process cannot be stabilized in a stored state because of the chaotic motion of the network. In this paper, a pinning control method focused on the chaotic neural network is proposed. The computer simulation proves that the chaos in the chaotic neural network can be controlled with this method and the states of the network can converge in one of its stored patterns if the control strength and the pinning density are chosen suitable. It is found that in general the threshold of the control strength of a controlled network is smaller at higher pinned density and the chaos of the chaotic neural network can be controlled more easily if the pinning control is added to the variant neurons between the initial pattern and the target pattern.
由混沌神经元构建的混沌神经网络具有联想记忆功能，但其记忆搜索过程会因网络的混沌运动而无法稳定在存储状态。本文提出了一种针对混沌神经网络的牵制控制方法。计算机仿真表明，采用该方法可以控制混沌神经网络中的混沌现象，并且如果选择合适的控制强度和牵制密度，网络状态能够收敛到其存储模式之一。研究发现，一般而言，在较高的牵制密度下，受控网络的控制强度阈值较小，并且如果在初始模式和目标模式之间的变异神经元上添加牵制控制，则混沌神经网络的混沌现象更容易得到控制。
