A Chaos Associative Model with a Sinusoidal Activation Function

Abstract

In this paper, we shall propose a novel chaos neural network model applied to the chaotic autoassociation memory. The present artificial neuron model is properly characterized in terms of a time-dependent periodic activation function to involve 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 retrieval beyond the conventional models with the nonmonotonous activation function as well as a monotonous activation function as the sigmoidal one. This advantage is found to result from the property of the analogue periodic mapping accompanied with a chaotic behaviour of the neurons. It is also concluded that the present analogue neuron model, with the periodicity control, has an apparently large memory capacity in comparison with the previously proposed association models.