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Multivariate data analysis method oriented to dynamic system model verification

A dynamic system model and multivariate data technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., to achieve the effect of avoiding verification results and eliminating influence

Inactive Publication Date: 2014-12-24
CHONGQING UNIV
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Problems solved by technology

[0008] In view of this, the object of the present invention is to provide a multivariate data analysis method for dynamic system model verification, which solves the problem of multivariate data dimensionality reduction and correlation processing for multivariate dynamic system verification, and can be applied to analyze the same dynamic system The degree of agreement between the multivariate response simulation model and the corresponding experimental data

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  • Multivariate data analysis method oriented to dynamic system model verification
  • Multivariate data analysis method oriented to dynamic system model verification
  • Multivariate data analysis method oriented to dynamic system model verification

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Embodiment Construction

[0026] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0027] figure 1 It is a schematic flow sheet of the method of the present invention, as shown in the figure:

[0028] This method comprises the following steps:

[0029] Step 1: PCA-based data dimensionality reduction, PCA analysis of multivariate data;

[0030] Normalized test data peak dimension data for comparability from test data and simulations. PCA is then applied to the standardized experimental data for dimensionality reduction and to address multivariate data related issues. The PCA coefficient matrix derived from the experimental data is then used to transform the simulated data for comparison with the experimental data in the same dimensionality reduction space.

[0031] Step 2: Error evaluation of dynamic response;

[0032] Use the EARTH error evaluation method to evaluate the PCA dimensionality reduction test and simulati...

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Abstract

The invention discloses a multivariate data analysis method oriented to dynamic system model verification and belongs to the technical field of model verification. The multivariate data analysis method includes the steps of firstly, subjecting standardized experimental data to data dimension reduction based on PCA (principal component analysis) and subjecting multivariate data to PCA; secondly, performing error assessment for dynamic responses; thirdly, computing response scores based on an SME (subject matter expert); fourthly, computing EEARTH (enhanced error assessment of response time histories) scores based on PCA; fifthly, enabling a decision maker to decide to accept or refuse a predicting result of a simulation model for a corresponding physical experiment. The multivariate data analysis method oriented to dynamic system model verification has the advantages that not only can time curve characteristics of the dynamic responses be analyzed comprehensively, but also a potential principal component of the multivariate data can be found out, influence of multivariate data correlativity on a verification result is eliminated, the verification result contradicting with the multivariate dynamic response quantity is avoided, and the problem of multivariate correlation dynamic response quantity analysis of a dynamic system is handled effectively.

Description

technical field [0001] The invention belongs to the technical field of model verification, and relates to a multivariate data analysis method for dynamic system model verification. Background technique [0002] Model verification is the process of evaluating the validity and accuracy of the CAE model for the intended use by comparing the output of the CAE model with the test results. Successful model validation can significantly reduce the investment in model building and testing. Complex product engineering system models often contain multiple output responses. When testing a dynamic system model, it is usually necessary to examine the influence of the data correlation of multivariate output responses on the model validation results. [0003] In multivariate data analysis, due to the large number of variables contained in multivariate data and the data correlation between dimensions, it is difficult to analyze the results and use multivariate statistical analysis. The ma...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
Inventor 詹振飞杨俊祺郑玲舒雅静
Owner CHONGQING UNIV
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