This paper proposes a data-driven set-based estimation algorithm for a class of nonlinear systems with polynomial nonlinearities. Using the system's input-output data, the proposed method computes in real-time a set that guarantees the inclusion of the system's state. Although the system is assumed to be polynomial type, the exact polynomial functions and their coefficients need not be known. To this end, the estimator relies on offline and online phases. The offline phase utilizes past input-output data to estimate a set of possible coefficients of the polynomial system. Then, using this estimated set of coefficients and the side information about the system, the online phase provides a set estimate of the state. Finally, the proposed methodology is evaluated through its application on SIR (Susceptible, Infected, Recovered) epidemic model.
翻译:本文为一类具有多元非线性的非线性系统提出了一个基于数据驱动的数据集估算算法。 使用该系统的输入- 输出数据, 拟议的方法实时计算一套保证纳入系统状态的数据集。 虽然假设该系统是多星型, 但确切的多元功能及其系数并不为人所知。 为此, 测算器依靠离线和在线阶段。 离线阶段利用过去的输入- 输出数据来估计多球系统的一系列可能的系数。 然后, 利用这套估计系数和关于系统的侧面信息, 在线阶段提供了一套状态估计。 最后, 通过在 SIR (可感知、 感染、 恢复) 流行模型上的应用来评估拟议方法。