The primary intention of the work is to develop a simple and universal method for identifying time-varying nonlinear dynamic systems. No prior knowledge of the structure (Hammerstein or Wiener) and parametric form of static nonlinear characteristics is required. We limit ourselves to the class of so-called ’fading memory systems’, i.e., those that can be approximated with arbitrary accuracy using finite memory models. The proposed identification algorithm is non-parametric in nature and is based on the κ nearest neighbor technique. The distance metric considered is dependent on both the values of the excitations and their timeliness. The original idea is to use an auto-tuning method for the κ parameter based on Efron’s bootstrap technique. This has been proven to guarantee optimal balance between variance and estimator bias.

中文翻译：

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使用基于自适应引导的跟踪识别时变非线性系统


这项工作的主要目的是开发一种简单且通用的方法来识别时变非线性动态系统。不需要静态非线性特性的结构（Hammerstein 或 Wiener）和参数形式的先验知识。我们将自己限制在所谓的“衰退记忆系统”类别中，即那些可以使用有限记忆模型以任意精度进行近似的系统。所提出的识别算法本质上是非参数的并且基于κ最近邻技术。所考虑的距离度量取决于激励的值及其及时性。最初的想法是使用基于 Efron 的 bootstrap 技术的 κ 参数自动调整方法。事实证明，这可以保证方差和估计偏差之间的最佳平衡。