Tracking a moving target using chaotic dynamics in a recurrent neural network model

Abstract

Chaotic dynamics introduced in a recurrent neural network model is applied to controlling an tracker to track a moving target in two-dimensional space, which is set as an ill-posed problem. The motion increments of the tracker are determined by a group of motion functions calculated in real time with firing states of the neurons in the network. Several groups of cyclic memory attractors that correspond to several simple motions of the tracker in two-dimensional space are embedded. Chaotic dynamics enables the tracker to perform various motions. Adaptively real-time switching of control parameter causes chaotic itinerancy and enables the tracker to track a moving target successfully. The performance of tracking is evaluated by calculating the success rate over 100 trials. Simulation results show that chaotic dynamics is useful to track a moving target. To understand them further, dynamical structure of chaotic dynamics is investigated from dynamical viewpoint. © 2008 Springer-Verlag Berlin Heidelberg.