论文标题
非线性系统标识的反馈
Feedback for nonlinear system identification
论文作者
论文摘要
由神经科学的神经元模型的促进，我们考虑了系统识别简单反馈结构的系统，其行为包括非线性现象，例如兴奋性，极限周期和混乱。我们表明，输出反馈足以在两步过程中解决识别问题。首先，提取了系统的非线性静态特征，其次，使用反馈线性化定律，确定了具有大约有限内存的轻度非线性系统。在理想的环境中，第二步归结为LTI系统的识别。为了在现实设置中说明该方法，我们提供了识别符合假定模型结构的两个经典系统的数值模拟。
Motivated by neuronal models from neuroscience, we consider the system identification of simple feedback structures whose behaviors include nonlinear phenomena such as excitability, limit-cycles and chaos. We show that output feedback is sufficient to solve the identification problem in a two-step procedure. First, the nonlinear static characteristic of the system is extracted, and second, using a feedback linearizing law, a mildly nonlinear system with an approximately-finite memory is identified. In an ideal setting, the second step boils down to the identification of a LTI system. To illustrate the method in a realistic setting, we present numerical simulations of the identification of two classical systems that fit the assumed model structure.
