论文标题
迈向改进的特征系统实现算法的低率保证
Towards an improved Eigensystem Realization Algorithm for low-error guarantees
论文作者
论文摘要
特征系统实现算法（ERA）是一种工具，可以从给定系统的输入输出数据中产生降低的订单模型（ROM）。 ERA创建ROM的同时将内部状态数量保持在最低水平。这是Juang and Pappa（1984）最初实施的，以分析脉冲响应中航空航天结构的振动。我们回顾了ERA，并在单个输入单输出（SISO）系统以及多个输入单输出（MISO）系统上对其进行了测试。 ERA预测与实际数据一致。与其他模型还原技术（平衡的截断，平衡的正交分解）不同，ERA在无需伴随系统的情况下工作也一样好，这使得ERA成为一种有希望的，完全数据驱动的，节俭的模型减少方法。在这项工作中，我们提出了一种修改的特征系统实现算法，该算法依赖于使用的输出最佳选择的时间分辨率，还可以通过频率分析检查良好的性能。讨论了四个示例：前两个确认了模型生成能力，最后两个说明了其产生低维模型（对于大型系统）的能力，该模型比传统时代生产的模型要准确得多。
Eigensystem Realization Algorithm (ERA) is a tool that can produce a reduced order model (ROM) from just input-output data of a given system. ERA creates the ROM while keeping the number of internal states to a minimum level. This was first implemented by Juang and Pappa (1984) to analyze the vibration of aerospace structures from impulse response. We reviewed ERA and tested it on single input single output (SISO) system as well as on multiple input single output (MISO) system. ERA prediction agreed with the actual data. Unlike other model reduction techniques (Balanced truncation, balanced proper orthogonal decomposition), ERA works just as fine without the need of the adjoint system, that makes ERA a promising, completely data-driven, thrifty model reduction method. In this work, we propose a modified Eigensystem Realization Algorithm that relies upon an optimally chosen time resolution for the output used and also checks for good performance through frequency analysis. Four examples are discussed: the first two confirm the model generating ability and the last two illustrate its capability to produce a low-dimensional model (for a large scale system) that is much more accurate than the one produced by the traditional ERA.
