Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Okayama, 700-8530, Japan,
Cogn Neurodyn. 2008 Mar;2(1):39-48. doi: 10.1007/s11571-007-9029-6. Epub 2007 Oct 9.
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.
将混沌动力学引入到一个递归神经网络模型中，用于控制一个物体在二维空间中跟踪一个移动目标，这被设定为一个不适定问题。物体的运动增量由一组运动函数决定，这些运动函数是根据网络中神经元的发射状态实时计算的。嵌入了几个与物体在二维空间中的几个简单运动相对应的循环记忆吸引子。网络中引入的混沌动力学导致物体在二维空间中产生相应的复杂运动。控制参数的自适应实时切换导致网络状态空间中的约束混沌（混沌遍历），使物体能够成功地沿着某个轨迹跟踪移动目标。通过对 100 次试验的成功率进行计算，评估了跟踪的性能，这些试验涉及目标分别沿着九种轨迹运动的情况。计算机实验表明，混沌动力学有助于跟踪移动目标。为了从动力学角度理解这些情况与混沌动力学之间的关系，从动力学角度研究了混沌动力学的结构。
