A chaos associative model with a skew-tent activation function

We propose a chaos neural network model applied to the chaotic auto-association memory. The artificial neuron model is properly characterized in terms of a time-dependent skew-tent periodic activation function to involve a chaotic dynamics as well as the energy steepest descent strategy. It is elucidated that the present neural network has a remarkable ability of dynamic memory retrievals beyond the conventional models with the nonmonotonous activation function as well as such a monotonous activation function as sigmoidal one. This feature results from the property of the analogue periodic mapping, accompanied by the chaotic behaviour of the neurons. It is also concluded that the present analogue neuron model with periodicity control has an apparently large memory capacity in comparison with the previously proposed association models.