a feed-forward back propagating neural network is trained to achieve and maintain control of the unstable periodic orbits embedded in a chaotic attractor on the inputs of parameter perturbation model.
设计前馈反传神经网络,通过对参数扰动模型输入样本的学习,训练成混沌控制器,将嵌入在混沌吸引子中不稳定周期轨道镇定到稳定不动点。
this is the process by which information can be hidden in the evolution of a chaotic attractor and retrieved by substracting the same chaotic background to reveal the original message.
在这个过程中需要传递的信息被隐藏在一个用来干扰的混沌系统中,接受方将信号减去相同的混沌背景即可得到原始信息。
the ogy algorithm and the prior iterate control algorithm to stabilize unstable fixed points embedded in a chaotic attractor for systems with delay coordinates are derived based on the pole placement.
基于极点配置推导出含延迟坐标系统镇定嵌入混沌吸引子中不稳定不动点的ogy控制算法和预迭代控制算法,从而具体地揭示了极点配置与控制混沌镇定方案之间的联系。