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Simplifying soft measurement method for primary variable in production process integrating KPLS (Kernel Partial Least Squares) and FNN (False Nearest Neighbors)

A technology of leading variables and measurement methods, applied in the field of soft measurement, can solve problems such as huge amount of calculation, time-consuming and labor-intensive

Active Publication Date: 2013-03-13
重庆重科加速创业孵化器有限公司
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Problems solved by technology

Computing many auxiliary variables to realize the soft measurement of the leading variable will bring a huge amount of calculation, which is not only time-consuming and labor-intensive, but also the obtained soft measurement results are not necessarily the best, which is in the generation process unwanted things

Method used

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  • Simplifying soft measurement method for primary variable in production process integrating KPLS (Kernel Partial Least Squares) and FNN (False Nearest Neighbors)
  • Simplifying soft measurement method for primary variable in production process integrating KPLS (Kernel Partial Least Squares) and FNN (False Nearest Neighbors)
  • Simplifying soft measurement method for primary variable in production process integrating KPLS (Kernel Partial Least Squares) and FNN (False Nearest Neighbors)

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Embodiment 1

[0064] Such as figure 1 , the combined KPLS and FNN production process leading variable streamlined soft-sensing method, proceed as follows:

[0065] Step 1: Determine n original auxiliary variables that may be related to the leading variable, collect the values ​​​​of n original auxiliary variables and leading variables, and form a sample set. The size of the sample set is m, and write the n original auxiliary variable data into a matrix Form, the leading variable data is written as a matrix Y=[y 1 ,...,y m ] T form, where x i ∈ R n×1 ,y i ∈R, i=1, 2,..., m, and further standardize them as follows to obtain the processed data matrix:

[0066]

[0067] Y = [ y 1 - Σ j = 1 ...

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Abstract

The invention discloses a simplifying soft measurement method for a primary variable in a production process integrating KPLS (Kernel Partial Least Squares) and FNN (False Nearest Neighbors). The method is characterized by comprising the following steps: determining n original auxiliary variables possibly related to the primary variable, collecting value data of the n original auxiliary variables and the primary variable, and forming a sample set; respectively calculating weighted values of the n original auxiliary variables by using a method of integrating the KPLS and the FNN; forming an original auxiliary variable sequence; modeling and determining the best auxiliary variable according to a minimum mean square error (MSE); and acquiring a simplifying soft measurement model. According to the method, an auxiliary variable set containing the auxiliary variables with the least number can be found for modeling the primary variable on the basis of the best modeling effect, so that the simplifying soft measurement on the primary variable can be realized.

Description

technical field [0001] The invention belongs to the technical field of soft sensing, and in particular relates to a soft sensing method for streamlining leading variables in a production process by fusing Kernel Partial Least Squares (KPLS) and false nearest neighbors (False Nearest Neighbors, FNN). Background technique [0002] So far, in the actual production process, there are many variables that cannot be directly measured due to technical or economic reasons. In this case, soft sensor technology emerges as the times require. Soft sensing is based on the mathematical relationship between measurable and easy-to-measure process variables (called auxiliary variables) and difficult-to-detect directly measured variables (called leading variables), according to a certain optimal criterion, using various calculation methods, using The software realizes the measurement or estimation of the measured variable. Soft sensor technology is a hot spot in current research. For example,...

Claims

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Application Information

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IPC IPC(8): G01D21/00
Inventor 苏盈盈姚立忠颜克胜李太福胡文金王美丹
Owner 重庆重科加速创业孵化器有限公司
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