几种数据驱动非线性系统辨识方法在简化糖调节系统示例中的比较
Comparison of several data-driven nonlinear system identification methods on a simplified glucoregulatory system example
AI总结：
本文比较了几种数据驱动的非线性辨识方法在简化糖调节系统建模中的应用，分析了不同方法的优缺点，并展示了其在人工胰腺开发中的有效性。
摘要：
本文比较了几种先进的数据驱动非线性辨识技术，针对一个特定问题：简化糖调节系统建模示例。该问题代表了开发人工胰腺用于1型糖尿病治疗中的挑战，因为该应用需要良好的非线性模型来设计精确的闭环控制器以调节血糖水平。块导向以及状态空间模型被用来描述胰岛素-葡萄糖系统的动态和非线性行为，并指出了每种方法的优缺点。所获得的非线性模型在模拟患者行为方面是准确的，其中一些模型也足够简单，可以用于开发人工胰腺的基于模型的控制器。
英文摘要：
In this paper, several advanced data-driven nonlinear identification techniques are compared on a specific problem: a simplified glucoregulatory system modeling example. This problem represents a challenge in the development of an artificial pancreas for T1DM treatment, since for this application good nonlinear models are needed to design accurate closed-loop controllers to regulate the glucose level in the blood. Block-oriented as well as state-space models are used to describe both the dynamics and the nonlinear behavior of the insulin-glucose system, and the advantages and drawbacks of each method are pointed out. The obtained nonlinear models are accurate in simulating the patient's behavior, and some of them are also sufficiently simple to be considered in the implementation of a model-based controller to develop the artificial pancreas.
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