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Neural network prediction method based on principal component analysis

A principal component analysis and neural network technology, applied in the field of automation industry, can solve the problems of low modeling accuracy, data redundancy, and high modeling complexity, and achieve the effects of reducing data dimensions, improving accuracy, and avoiding redundancy

Inactive Publication Date: 2019-07-26
HANGZHOU DIANZI UNIV +1
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Problems solved by technology

[0003] Aiming at the problems of data redundancy, high modeling complexity and low modeling precision in traditional algorithms in data processing, the present invention proposes a neural network prediction method based on principal component analysis

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  • Neural network prediction method based on principal component analysis
  • Neural network prediction method based on principal component analysis
  • Neural network prediction method based on principal component analysis

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Embodiment Construction

[0044] The present invention will be further described below.

[0045] Take the ethylene oxidation reactor as an example:

[0046] The ethylene reactor is a fixed-bed tubular reactor. The raw materials ethylene and oxygen pass through the reactor continuously, and directly react on the surface of the catalyst to generate ethylene oxide. The yield is a key control index, and the yield prediction of the ethylene oxidation reactor is established. The model takes six measurable variables as the input variables of the reactor yield prediction model, and takes the reactor yield as the output of the model.

[0047] Step 1. Collect the variables affecting the yield of the ethylene oxidation reactor and the yield of the reactor, and process the data by principal component analysis. The specific steps are:

[0048] 1-1. Define the first principal component of the variable affecting the yield of the ethylene oxidation reactor in the following form:

[0049] t 1 =Xp 1 =[v 1 v 2 .....

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Abstract

The invention discloses a neural network prediction method based on principal component analysis, and the method comprises the following steps: 1, collecting process data and quality data, and carrying out the processing of the data through the principal component analysis; and step 2, establishing a neural network model by using the data obtained in the step 1, and performing prediction. According to the method, firstly, process variables and quality variables generated in the chemical process are collected, a principal component analysis method is used for preprocessing data, the data dimension is reduced, redundancy is avoided, the processed data are input into a prediction model of the radial basis function neural network, corresponding parameters are solved and optimized, and the model prediction accuracy reaches a preset value. Different from a traditional prediction method, the method combines a principal component analysis method and a radial basis function neural network model, reduces the complexity of modeling, and improves the precision of the model.

Description

technical field [0001] The invention belongs to the technical field of automation industry and relates to a neural network prediction method based on principal component analysis. Background technique [0002] With the continuous maturity and complexity of modern industrial processes, more and more industrial process information can be collected. However, in some chemical processes, important variables can only be adjusted based on off-line analysis values, and the time lag is large, making industrial process control difficult. If these variables cannot be obtained in real time, not only the required performance of the system cannot be guaranteed, but even the production capacity and quality stability of the chemical plant will be directly affected. In order to obtain the variables that are difficult to measure in the chemical process in real time and achieve precise control, a neural network prediction method based on principal component analysis is proposed. Contents of...

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Application Information

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IPC IPC(8): G06N3/08G06K9/62
CPCG06N3/08G06F18/2135G06F18/23213
Inventor 张日东欧丹林吴胜袁亦斌高福荣
Owner HANGZHOU DIANZI UNIV
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