Statistical Properties of Chaos Associative Memory
Masahiro Marshall Nakagawa · Journal of the Physical Society of Japan · 2002
In this paper we shall investigate the statistical property and the memory capacity of the chaotic autoassociation memory. The present artificial neuron model is properly characterized in terms of a timedependent sinusoidal activation function to involve a transient chaotic dynamics as well as the energy steepest descent strategy. It is elucidated that the present neural network has a remarkable retrieval ability beyond the conventional models with such a monotonous activation function as sigmoidal one. This advantage is found to result from the property of the analogue periodic mapping accompanied with a chaotic behaviour of the neurons as well as the symmetry of the dynamic equation which may be shown in the invariant measure determined by the Frobenius-Perron equation.