Continuous-time nonlinear signal processing: a neural network based approach for gray box identification
Continuous-time nonlinear signal processing: a neural network based approach for gray box identification
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DOI：
10.1109/nnsp.1994.366006
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发表时间：
1994-09
期刊：
影响因子：
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通讯作者：
R. Rico-Martínez;J. S. Anderson;I. Kevrekidis
中科院分区：
文献类型：
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作者：
R. Rico-Martínez;J. S. Anderson;I. Kevrekidis
人工神经网络（ANN）通常用于短期离散时间序列预测。连续时间模型，但是，需要定性正确的近似长期动态（吸引子）的非线性动力系统及其过渡（分叉）作为系统参数是不同的。在以前的工作中，作者开发了一个黑箱方法的特征的实验时间序列的连续时间模型（常微分方程组）的基础上的神经网络平台。这种方法自然适用于部分已知的第一原理动态模型的识别，在这里，作者将其扩展到“灰箱”识别。
Artificial neural networks (ANNs) are often used for short term discrete time series predictions. Continuous-time models are, however, required for qualitatively correct approximations to long-term dynamics (attractors) of nonlinear dynamical systems and their transitions (bifurcations) as system parameters are varied. In previous work the authors developed a black-box methodology for the characterization of experimental time series as continuous-time models (sets of ordinary differential equations) based on a neural network platform. This methodology naturally lends itself to the identification of partially known first principles dynamic models, and here the authors present its extension to "gray-box" identification.>