Mixed auxiliary variable separation and dimension reduction method based on independent subspace false neighboring point discrimination

An independent subspace and auxiliary variable technology, applied in the field of soft measurement, can solve the problem that the original feature space is difficult to original feature and reduce, and achieve the effect of reducing complexity, improving efficiency, and saving human, material and financial resources.

Inactive Publication Date: 2013-07-17
CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0003] Since auxiliary variables are usually mixed signals of multiple factors, it is difficult to perform original feature reduction in the original feature space

Method used

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  • Mixed auxiliary variable separation and dimension reduction method based on independent subspace false neighboring point discrimination
  • Mixed auxiliary variable separation and dimension reduction method based on independent subspace false neighboring point discrimination
  • Mixed auxiliary variable separation and dimension reduction method based on independent subspace false neighboring point discrimination

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Embodiment 1

[0047] Step 1: Assume that there is a stable inherent discriminant model in the data source: y=c 1 +c 2 x 4 +c 3 x 5 +ε, construct the independent variable matrix X=(x 1 , x 2 , x 3 , x4 , x 5 ), the sample size is 60 groups, as shown in Table 1. where x 4 , x 5 Satisfy the independent standard normal distribution, in this case c 1 =51,c 2 = 3,c 3 =4.

[0048] Table 1 Source signal parameters X

[0049]

[0050]

[0051] Step 2: The method of discriminating false neighbors based on independent subspace is mainly based on ICA (Independent Components Analysis), with the help of FNN (False Nearest Neighbors) method to calculate n original auxiliary variables respectively weight value;

[0052] According to the source signal parameter matrix in Table 1, calculate x 1 , x 2 , x 3 , x 4 , x 5 The eigenvalues ​​and eigenvectors of the covariance matrix, the results are shown in Table 2, and the cumulative contribution rate of λ is calculated according to the...

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Abstract

The invention discloses a mixed auxiliary variable separation and dimension reduction method based on independent subspace false neighboring point discrimination. The mixed auxiliary variable separation and dimension reduction method based on the independent subspace false neighboring point discrimination is characterized by including the following steps: 1, determining n original auxiliary variables probably related a primary variable, and acquiring value data of the n original auxiliary variables and the primary variable to form a sample set; 2, respectively calculating weighing values of the n original auxiliary variables through the independent subspace false neighboring point discrimination; 3, forming an original auxiliary variable sequence; 4, utilizing a least square regression method to build a model, and determining the best auxiliary variable according to a minimum mean square error (MSE); 5, obtaining separated independent signal soft measurement model. The mixed auxiliary variable separation and dimension reduction method based on the independent subspace false neighboring point discrimination can find a variable set containing mixed auxiliary variables on the basis of the optical modeling effect to perform separation, achieves dimension reduction, simplifies auxiliary variable information, simultaneously reduces model complexity, and improves soft measurement effectiveness.

Description

technical field [0001] The invention belongs to the field of soft sensor technology, and in particular relates to a separation and dimensionality reduction method of mixed auxiliary variables based on independent subspace false neighbor discrimination, which is used to guide the separation of mixed auxiliary variables and the dimensionality reduction of complex soft sensor models. Background technique [0002] Some key parameters in industrial production cannot be obtained accurately by conventional measurement methods, and the use of soft measurement technology can effectively solve this problem. However, the number of original auxiliary variables in the soft sensor method is redundant and complex, showing the characteristics of mixed signals, resulting in an exponential increase in the complexity of the model with the increase of the original auxiliary variables, and the problem of the curse of dimensionality of the model. Therefore, how to select the optimal feature subse...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 苏盈盈刘兴华葛继科颜克胜曾诚
Owner CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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