CONTROLLING CHAOS IN CHAOTIC NEURAL NETWORKS

This work demonstrates the control of chaos in chaotic neural networks . Chaotic neural networks, which were proposed by Aihara and others, c onsist of chaotic neuron models, and are based on research on the gian t axon of squids and study of the Hodgkin-Huxley equation. They show a chaotic response that cannot be expressed by conventional neuron mode ls. Although research has been performed on utilizing this chaotic res ponse for active search of associative memory, it is difficult to dete rmine when to stop the chaotic dynamics. Therefore, we have recently i nvestigated chaotic control methods that had been the subject of previ ous research. Among these control methods, there is a method in which unstable period points are stabilized by multiplying the exponent or e xponential function by the system parameters. We used an improved vers ion of this method for high-dimension system control. For simplicity, we describe the control of networks that are coupled with the first or der nearest neighbors and have global homogeneous coupling. We confirm that by using this control method, the unstable period points in the network can be stabilized and control of chaos is possible. With this method, stabilization is also possible when noise is added to the syst em. (C) 1998 Scripta Technica.