The paper discusses the identification of the empirical, partially intelligible white noise processes generated by deterministic numerical algorithms. The introduced fuzzy-random complementary approach can identify the inner hidden correlational patterns of the empirical white noise process if the process has a real hidden structure of this kind. We have shown how the characteristics of autocorrelated white noise processes change as the order of autocorrelation increases. Based on this approach, the original empirical white noise process transformed by the autocorrelation operator can be considered to be random data series (randomlikeness), and at the same time, it has function-like characteristics (functionlikeness), as well. We approach the analysis of the mentioned complementarity by modeling the autocorrelation functions of the empirical white noise processes using tensor product (TP) model transformation.

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经验部分可理解白噪声过程的建模与辨识

本文讨论了确定性数值算法生成的经验性、部分可理解的白噪声过程的识别。如果该过程具有此类真实隐藏结构，则引入的模糊随机互补方法可以识别经验白噪声过程的内部隐藏相关模式。我们已经展示了自相关白噪声过程的特征如何随着自相关阶数的增加而变化。基于这种方法，经过自相关算子变换的原始经验白噪声过程可以被认为是随机数据序列（randomlikeness），同时它也具有类函数特性（functionlikeness）。