论文标题
关于最佳线性近似的非线性LFR模型识别的初始化
On the Initialization of Nonlinear LFR Model Identification with the Best Linear Approximation
论文作者
论文摘要
平衡模型复杂性和捕获过程的表示能力仍然是非线性系统识别的主要挑战之一。降低模型复杂性的一种可能性是将结构强加于模型表示。为此，这项工作考虑了线性分数表示框架。在线性分数表示中，线性动力学和系统非线性由两个彼此互连的两个独立块建模。这导致结构化但灵活的模型结构。直接从输入输出数据估算这种模型并不是一个琐碎的任务，因为所涉及的优化本质上是非线性的。本文根据系统的最佳线性近似提出了模型参数的初始化方案，并表明此方法在一组基准数据集中导致高质量模型。
Balancing the model complexity and the representation capability towards the process to be captured remains one of the main challenges in nonlinear system identification. One possibility to reduce model complexity is to impose structure on the model representation. To this end, this work considers the linear fractional representation framework. In a linear fractional representation the linear dynamics and the system nonlinearities are modeled by two separate blocks that are interconnected with one another. This results in a structured, yet flexible model structure. Estimating such a model directly from input-output data is not a trivial task as the involved optimization is nonlinear in nature. This paper proposes an initialization scheme for the model parameters based on the best linear approximation of the system and shows that this approach results in high quality models on a set of benchmark data sets.
