Department of Electronic & Information System Engineering, The Graduate School of Natural Science & Technology, Okayama University, 700-8530, Okayama, Japan.
Cogn Neurodyn. 2007 Sep;1(3):189-202. doi: 10.1007/s11571-006-9009-2. Epub 2006 Dec 7.
Chaotic dynamics in a recurrent neural network model and in two-dimensional cellular automata, where both have finite but large degrees of freedom, are investigated from the viewpoint of harnessing chaos and are applied to motion control to indicate that both have potential capabilities for complex function control by simple rule(s). An important point is that chaotic dynamics generated in these two systems give us autonomous complex pattern dynamics itinerating through intermediate state points between embedded patterns (attractors) in high-dimensional state space. An application of these chaotic dynamics to complex controlling is proposed based on an idea that with the use of simple adaptive switching between a weakly chaotic regime and a strongly chaotic regime, complex problems can be solved. As an actual example, a two-dimensional maze, where it should be noted that the spatial structure of the maze is one of typical ill-posed problems, is solved with the use of chaos in both systems. Our computer simulations show that the success rate over 300 trials is much better, at least, than that of a random number generator. Our functional simulations indicate that both systems are almost equivalent from the viewpoint of functional aspects based on our idea, harnessing of chaos.
混沌动力学在递归神经网络模型和二维细胞自动机中都得到了研究，这两个模型都具有有限但自由度很大的特点，从利用混沌的角度进行了研究，并应用于运动控制，表明它们都具有通过简单规则控制复杂功能的潜力。一个重要的观点是，这两个系统中产生的混沌动力学为我们提供了自主的复杂模式动力学，这些动力学在高维状态空间中遍历嵌入模式（吸引子）之间的中间状态点。基于这样一种思想，即利用在弱混沌状态和强混沌状态之间的简单自适应切换，可以提出将这些混沌动力学应用于复杂控制的方法。作为一个实际的例子，我们使用这两个系统中的混沌来解决二维迷宫问题，需要注意的是，迷宫的空间结构是一个典型的不适定问题。我们的计算机模拟表明，在 300 多次试验中，成功率至少比随机数发生器要好得多。从我们的观点出发，基于功能方面的考虑，我们的功能模拟表明，这两个系统几乎是等效的，即利用混沌。
