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Sample ingredient content measuring method based on online sequential limit learning machine

An extreme learning machine and determination method technology, which is applied in the field of sample component content determination, can solve the problems of slow algorithm modeling, cannot be processed block by block, generalization performance is general, etc., so as to improve the modeling speed and reduce repeated calculation. Quantity, the effect of improving accuracy and generalization performance

Active Publication Date: 2018-04-17
东北大学秦皇岛分校
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  • Claims
  • Application Information

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Problems solved by technology

[0004] The object of the present invention is to provide a method for determining the content of sample components based on an online sequential extreme learning machine, which can effectively solve the problems in the prior art, especially the existing algorithm with slow modeling speed and generalization performance. And it can only process the new data one by one, but not block by block.

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  • Sample ingredient content measuring method based on online sequential limit learning machine
  • Sample ingredient content measuring method based on online sequential limit learning machine
  • Sample ingredient content measuring method based on online sequential limit learning machine

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Embodiment 1

[0058] Embodiment 1 of the present invention: a method for determining the content of sample components based on an online sequential extreme learning machine, such as Figure 16 As shown, the spectral data samples of the sample were collected and modeled using the online sequential extreme learning machine algorithm; the component content of the sample was determined using the established model.

[0059] Specifically, the following steps may be included:

[0060] S1, according to the initial master spectrum SP master(0) and the corresponding sample component content y 0 and the number of hidden layer nodes L, calculate the initial weight matrix α from the hidden layer to the output layer (0) , where SP master(0) and y 0 Contains M 0 samples;

[0061] S2, when there is a new master spectrum SP master(k+1) and the corresponding sample component content y k+1 When it arrives, the weight matrix α from the hidden layer to the output layer is calculated according to the onl...

Embodiment 2

[0092] Embodiment 2: A method for determining the component content of a sample based on an online sequential extreme learning machine, collecting spectral data samples of the sample, and using the online sequential extreme learning machine algorithm to model; using the established model to measure the component content of the sample .

[0093] Specifically, the following steps may be included:

[0094] S1, according to the initial master spectrum SP master(0) and the corresponding sample component content y 0 and the number of hidden layer nodes L, calculate the initial weight matrix α from the hidden layer to the output layer (0) , where SP master(0) and y 0 Contains M 0 samples;

[0095] S2, when there is a new master spectrum SP master(k+1) and the corresponding sample component content y k+1 When it arrives, the weight matrix α from the hidden layer to the output layer is calculated according to the online sequential extreme learning machine algorithm (k+1) ; Among...

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Abstract

The invention discloses a sample ingredient content measuring method based on an online sequential limit learning machine. The method comprises the following steps that: collecting the spectroscopic data sample of a sample, and utilizing an online sequential limit learning machine algorithm for modeling; and utilizing an established model to measure the ingredients of the sample. The online sequential limit learning machine algorithm is used for modeling, data which is previously used can not be kept, and only knowledge which is learnt previously is kept for standby; and when new spectroscopicdata arrives each time, the implicit strata output of new data needs to be calculated, and then, the knowledge which is learnt previously is used for dynamically updating output weight between a middle implicit strata and the output layer to carry out quick modeling. Compared with a traditional modeling method, the method disclosed by the invention improves modeling speed, reduces unnecessary repeated calculation amounts and consumption for a data storage space and improves the accuracy and the generalization performance of the model, in addition, data which arrives one by one can be processed, and data which can arrives by one block can be processed.

Description

technical field [0001] The invention relates to a sample component content determination method based on an online sequential extreme learning machine, and belongs to the technical field of sample component content determination. Background technique [0002] Near-infrared spectroscopy technology is a fast, non-destructive and low-cost indirect analysis technology. The near-infrared spectrum of the sample can be quickly measured by using an infrared spectrometer, and combined with the method of chemometrics, the near-infrared spectrum and effective component content of the sample can be established. The multivariate calibration model between them can predict the response components of unknown samples. However, in actual use, the near-infrared spectral data is not generated at one time, but in a streaming manner. If a model has been established on existing data samples, new data samples may be generated as time changes. In order to improve the generalization performance and ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
CPCG16Z99/00
Inventor 单鹏赵煜辉张贝淳宝生
Owner 东北大学秦皇岛分校
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