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Nonlinear FIR Identification with Model Order Reduction Steiglitz-McBride ?

机译：减少模型阶数的非线性FIR识别Steiglitz-McBride ？

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摘要

In system identification, many structures and approaches have been proposed to deal with systems with non-linear behavior. When applicable, the prediction error method, analogously to the linear case, requires minimizing a cost function that is non-convex in general. The issue with non-convexity is more problematic for non-linear models, not only due to the increased complexity of the model, but also because methods to provide consistent initialization points may not be available for many model structures. In this paper, we consider a non-linear rational finite impulse response model. We observe how the prediction error method requires minimizing a non-convex cost function, and propose a three-step least-squares algorithm as an alternative procedure. This procedure is an extension of the Model Order Reduction Steiglitz-McBride method, which is asymptotically efficient in open loop for linear models. We perform a simulation study to illustrate the applicability and performance of the method, which suggests that it is asymptotically efficient.
机译：在系统识别中，已经提出了许多结构和方法来处理具有非线性行为的系统。当适用时，类似于线性情况，预测误差方法要求最小化通常不为凸的成本函数。对于非线性模型，非凸性问题更加棘手，这不仅是因为模型的复杂性增加，而且因为提供一致的初始化点的方法可能不适用于许多模型结构。在本文中，我们考虑了非线性有理有限脉冲响应模型。我们观察到预测误差方法如何要求最小化非凸成本函数，并提出了三步最小二乘算法作为替代过程。此过程是模型降阶Steiglitz-McBride方法的扩展，该方法在线性模型的开环中渐近有效。我们进行了仿真研究，以说明该方法的适用性和性能，这表明该方法是渐近有效的。

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