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
复制标题
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:
影响因子:
--
通讯作者:
Dürrbaum
中科院分区:
文献类型:
--
作者:
Dürrbaum
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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DOI:
10.5555/2263019.2263030
发表时间:
2012
期刊:
Appl. Soft Comput.
影响因子:
--
作者:
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通讯作者:
Susanne Zaglauer
影响因子:
4.3
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发表时间:
2011
期刊:
Proceedings of the 2011 American Control Conference
影响因子:
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DOI:
10.1109/acc.2011.5990780
发表时间:
2011
期刊:
Proceedings of the 2011 American Control Conference
影响因子:
--
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1969
期刊:
--
影响因子:
--
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