@misc{cogprints3001, title = {Controlling chaos in a chaotic neural network}, author = {Dr G. He and Prof. Z. Cao and Prof. P. Zhu and Prof. H. Ogura}, publisher = {Elsevier Science Ltd.}, year = {2003}, journal = {Neural Networks}, keywords = {Chaotic dynamic; Chaotic neural network; Controlling chaos; Pinning control method}, url = {http://cogprints.org/3001/}, abstract = {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.} }