Periodic chaos neural network with autocorrelation dynamics

In this paper a novel chaos neural network model is proposed and applied to memory search and the autoassociation. The proposed artificial neuron model is substantially characterized in terms of a time-dependent periodic activation function to involve a chaotic dynamics on the basis of the energy steepest descent strategy. It is elucidated that the present neural network has an ability of the dynamic memory retrievals beyond the conventional models with the nonmonotonous activation function as well as such a monotonous activation function as sigmoidal one. This advantage results from the nonmonotonous property of the analogue periodic mapping accompanied with a chaotic behaviour of the neurons. It is also found that the present analogue neuron model with the periodicity control has a remarkably large memory capacity in comparison with the previously proposed association models.