Maintaining chaos in an associative chaotic neural network exhibiting intermittency
Masaharu Adachi · 2004
The paper presents an attempt to maintain chaos in an associative chaotic neural network, which exhibits intermittency without control. The network to be controlled is composed of 16 chaotic neurons with synaptic weights that are determined by a conventional auto-associative matrix to store three orthogonal patterns. The network shows intermittency with certain parameter values without control. In this paper, the network with the intermittency is controlled in order to maintain chaos in the network. The control is applied only when the state vector comes to the neighborhood of the point just before it falls into the laminar phase. Perturbations are applied so that the state vector may not fall into the laminar phase. An example of maintaining chaos with the control is shown in the paper.