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Weighted Principal Component Analysis for Wiener System Identification - Regularization and non-Gaussian Excitations

机译：维纳系统识别的加权主成分分析-正则化和非高斯激发

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摘要

Finite impulse response (FIR) Wiener systems driven by Gaussian inputs can be efficiently identified by a well-known correlation-based method, except those involving even static nonlinearities. To overcome this defficiency, another method based on weighted principal component analysis (wPCA) has been recently proposed. Like the correlation-based method, the wPCA is designed to estimate the linear dynamic subsystem of a Wiener system without assuming any parametric form of the nonlinearity. To enlarge the applicability of this method, it is shown in this paper that high order FIR approximation of IIR Wiener systems can be efficiently estimated by controlling the variance of parameter estimates with regularization techniques. The case of non-Gaussian inputs is also studied by means of importance sampling.
机译：高斯输入驱动的有限脉冲响应（FIR）Wiener系统可以通过众所周知的基于相关性的方法进行有效识别，除了那些甚至涉及静态非线性的系统。为了克服这种不足，最近提出了另一种基于加权主成分分析（wPCA）的方法。与基于相关的方法类似，wPCA被设计为估计维纳系统的线性动态子系统，而无需假设任何参数形式的非线性。为了扩大该方法的适用性，本文表明通过使用正则化技术控制参数估计的方差可以有效地估计IIR Wiener系统的高阶FIR近似。还通过重要性抽样研究了非高斯输入的情况。

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