A Parameter Controlled Chaos Neural Network
Masahiro Marshall Nakagawa · Journal of the Physical Society of Japan · 1996
In this paper we shall propose a simple chaos neural network model applied to autonomous chaotic memory searching and the autoassociation. The present artificial neuron model is properly characterized in terms of a sinusoidal activation function to involve a chaotic dynamics as well as an autonomous control of the periodicity. It is elucidated that the present neural network has an ability of the dynamic memory retrievals beyond the conventional chaotic model with such a monotonous mapping as a sigmoid function. This advantage is found to result 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 autonomous periodicity control has a remarkably large memory capacity in comparison with the conventional monotonous dynamics.