On optimal experiment design for identifying premise and conclusion parameters of Takagi-Sugeno models: Nonlinear regression case

On optimal experiment design for identifying premise and conclusion parameters of Takagi-Sugeno models: Nonlinear regression case
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用于识别 Takagi-Sugeno 模型前提和结论参数的最佳实验设计:非线性回归案例

DOI:
10.1016/j.asoc.2017.07.015
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发表时间:
2017
期刊:
Appl. Soft Comput.
影响因子:
--
通讯作者:
Dürrbaum
Dürrbaum
中科院分区:
--
文献类型:
--
作者:
Dürrbaum

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最优试验设计(OED)是一个成熟的概念,回归问题是线性的参数。在识别非线性Takagi-Sugeno(TS)模型的实验设计的情况下,已经提出了非基于模型的方法或限制于局部模型参数的OED(假设分区被给定)。本文提出了一种基于Fisher信息矩阵的OED方法,该方法考虑了局部模型和分区参数。由于非线性模型的原因,该模型依赖于随后识别的模型参数。为了解决这个矛盾的情况下,首先进行无模型的空间填充设计(如拉丁超立方体抽样)。所收集的数据允许进行设计决策,例如确定局部模型的数量和识别初始TS模型的参数。该初始TS模型允许基于FIM的OED,使得收集对于TS模型最优的数据。第一阶段的估计一般来说并不理想。为了对参数不匹配具有鲁棒性,采用序贯优化设计。在这项工作中,重点是D-最优设计。所提出的方法证明了三个非线性回归问题:工业轴流压缩机和两个测试功能。
Optimal Experiment Design (OED) is a well-developed concept for regression problems that are linear-in-the-parameters. In case of experiment design to identify nonlinear Takagi-Sugeno (TS) models, non-model-based approaches or OED restricted to the local model parameters (assuming the partitioning to be given) have been proposed. In this article, a Fisher Information Matrix (FIM) based OED method is proposed that considers local model and partition parameters. Due to the nonlinear model, the FIM depends on the model parameters that are subject of the subsequent identification. To resolve this paradoxical situation, at first a model-free space filling design (such as Latin Hypercube Sampling) is carried out. The collected data permits making design decisions such as determining the number of local models and identifying the parameters of an initial TS model. This initial TS model permits a FIM-based OED, such that data is collected which is optimal for a TS model. The estimates of this first stage will in general not be ideal. To become robust against parameter mismatch, a sequential optimal design is applied. In this work the focus is on D-optimal designs. The proposed method is demonstrated for three nonlinear regression problems: an industrial axial compressor and two test functions.
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