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Dimension Embedded Basis Function for Spatiotemporal Modeling of Distributed Parameter System

机译:尺寸嵌入式基础函数用于分布式参数系统的时空建模

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摘要

The construction of spatial basis functions (BFs) is critical to the time/space separation of the distributed parameter system (DPS). The spatial BFs constructed by traditional Karhunen-Loeve may not work satisfactorily for two spatial-dimensional (2-D) DPS, because of a distorted mapping of the original sensor array in the row-wise vectorization process. In this article, a novel time/space separation based method is proposed to construct dimension embedded BFs (DE-BFs) for modeling 2-D DPS. The DE-BFs are first formulated according to the spatial sensor array structure, and sequentially optimized with alternating least squares by minimizing the reconstruction error. The mapping relationship between the DE-BFs and the spatial sensor array is well preserved. In addition, the coupling across temporal and different spatial dimensions is sufficiently captured. A satisfactory model accuracy can be achieved by the DE-BFs, even with limited training data. Experiments of a 2-D curing thermal process are used to verify the effectiveness of the proposed method.
机译:空间基函数(BFS)的构造对于分布式参数系统(DPS)的时间/空间分离至关重要。由传统的Karhunen-Loeve构造的空间BFS可能无法令人满意地为两种空间(2-D)DPS工作,因为在行方向矢量化过程中的原始传感器阵列的扭曲映射。在本文中,提出了一种新的时间/空间分离方法来构建用于建模2-D DP的尺寸嵌入式BFS(DE-BFS)。首先根据空间传感器阵列结构首先配制DE-BF,并通过最小化重建误差来顺序地优化,并且通过最小化重建误差来利用交替的方块进行优化。 DE-BFS和空间传感器阵列之间的映射关系得到良好的保留。另外,充分捕获跨越时间和不同空间尺寸的耦合。即使具有有限的训练数据,DE-BF也可以实现令人满意的模型精度。使用2-D固化热处理的实验用于验证所提出的方法的有效性。

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