Wright Jon
Institute for Nonlinear Science, University of California at San Diego, La Jolla, California 92093-0402.
Chaos. 1995 Jun;5(2):356-366. doi: 10.1063/1.166106.
We extend our method for classifying signals from chaotic nonlinear dynamical systems to the problem of monitoring chaotic nonlinear dynamical systems with the goal of detecting that the state of a system has changed. One potential application would be to systems where the changes are not easily detectable by spectral analysis or other linear techniques. The method is expected to be most useful in comparison to other techniques when there are other signals or noise present, some of which have a broad band frequency spectrum, and the signal of interest is associated with either a low dimensional dynamical system or a low dimensional chaotic attractor. The method is applied to data from a laboratory model of a fluidized bed reactor and to data from a gyroscope as well as to numerically generated signals from mathematical models. For the dynamical systems considered in the paper, the proposed method provides significantly better discrimination than spectral analysis. (c) 1995 American Institute of Physics.
我们将用于对混沌非线性动力系统的信号进行分类的方法扩展到监测混沌非线性动力系统的问题，目标是检测系统状态是否发生了变化。一个潜在的应用领域是那些通过频谱分析或其他线性技术不易检测到变化的系统。当存在其他信号或噪声（其中一些具有宽带频谱），且感兴趣的信号与低维动力系统或低维混沌吸引子相关联时，预计该方法与其他技术相比最为有用。该方法应用于来自流化床反应器实验室模型的数据、来自陀螺仪的数据以及来自数学模型的数值生成信号。对于本文所考虑的动力系统，所提出的方法比频谱分析具有显著更好的辨别能力。(c) 1995美国物理研究所。
