The article addresses the problem of nonlinear system identification with particular focusing on Wiener models. The proposed input injection methodology allows for identification of a working system—without stopping its usual operation, production processes, and so on. The only interferences are the slight random injections added to the input signal, which—by assumption—do not disturb the overall system's functionality. Such input injections allow to limit the curse of dimensionality issues, particularly troublesome in many approaches proposed in the literature for the Wiener system identification. Furthermore, all the requirements concerning the applicability of the method are rather mild. In particular, it is assumed that the static nonlinear characteristic is of nonparametric form and the existence of its two derivatives is needed for the consistency of the proposed estimate. The class of admissible output noises is also rather wide and does not exclude processes correlated with inputs signals.

中文翻译：

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通过输入注入法识别维纳系统

本文针对非线性系统识别问题，特别关注Wiener模型。提议的输入注入方法可以识别工作系统，而无需停止其常规操作，生产过程等。唯一的干扰是添加到输入信号上的轻微随机注入，假设不干扰整个系统的功能。这样的输入注入允许限制维数问题的诅咒，在文献中为维纳系统识别提出的许多方法中特别麻烦。此外，关于该方法的适用性的所有要求都相当温和。尤其是，假定静态非线性特性为非参数形式，并且需要两个导数的存在才能保证所估计的一致性。允许的输出噪声的类别也相当广泛，并且不排除与输入信号相关的过程。