Novel tracking function of moving target using chaotic dynamics in a recurrent neural network model. Cogn Neurodyn 2:39-48

Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Okayama, 700-8530, Japan, .
Cognitive Neurodynamics (Impact Factor: 1.67). 04/2008; 2(1):39-48. DOI: 10.1007/s11571-007-9029-6
Source: PubMed


Chaotic dynamics introduced in a recurrent neural network model is applied to controlling an object to track a moving target in two-dimensional space, which is set as an ill-posed problem. The motion increments of the object are determined by a group of motion functions calculated in real time with firing states of the neurons in the network. Several cyclic memory attractors that correspond to several simple motions of the object in two-dimensional space are embedded. Chaotic dynamics introduced in the network causes corresponding complex motions of the object in two-dimensional space. Adaptively real-time switching of control parameter results in constrained chaos (chaotic itinerancy) in the state space of the network and enables the object to track a moving target along a certain trajectory successfully. The performance of tracking is evaluated by calculating the success rate over 100 trials with respect to nine kinds of trajectories along which the target moves respectively. Computer experiments show that chaotic dynamics is useful to track a moving target. To understand the relations between these cases and chaotic dynamics, dynamical structure of chaotic dynamics is investigated from dynamical viewpoint.

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    • "Furthermore, the idea is extended to challenging application of chaotic dynamics in control. Chaotic dynamics introduced in a recurrent network model was applied to control tasks that an object should solve a two-dimensional maze for catching a target (Suemitsu and Nara 2004), or should capture a target moving along different trajectories (Li and Nara 2008). A simple coding method is employed to project the higher dimensional neural states dynamics into lower dimensional motion increments. "
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