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Training test method of BP neural network regression model and application system thereof

A BP neural network and regression model technology, applied in the field of product processing and manufacturing, can solve problems such as insufficient generalization ability and poor neural network model training effect, and achieve the effect of reducing data redundancy

Inactive Publication Date: 2018-01-26
GUANGDONG UNIV OF TECH
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Problems solved by technology

However, when the data in the training set is too concentrated or too small, the training effect of the neural network model is often not good, that is, the generalization ability is insufficient. Therefore, it is important to maximize the training effect of the neural network for this kind of training set. practical significance

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  • Training test method of BP neural network regression model and application system thereof
  • Training test method of BP neural network regression model and application system thereof
  • Training test method of BP neural network regression model and application system thereof

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Embodiment Construction

[0045] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and through specific implementation methods.

[0046] The invention proposes a BP neural network regression model training and testing method combined with PCA, which is used for the prediction of the incision width of the oak laser cutting system, and realizes that the training effect of the BP neural network is effective when the training set data is too concentrated or too small. improved, such as figure 1 shown, including the following steps:

[0047] S10: Acquire data, and obtain a calibrated five-dimensional data set R of N experimental samples, which contains a total of M sets of experimental data and corresponding experimental results.

[0048] Preferably, said acquired data includes:

[0049] For the oak laser cutting system, the incision width is obtained under different laser energy, cutting speed, defocus, oak moisture content, and...

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Abstract

The invention relates to a training test method of a BP neural network regression model and is applied to an oak laser cutting system to predict notch widths. The method mainly comprises steps that a,data acquisition, data sets of N experiment samples are acquired, and M sets of experiment data are totally included; b, data pre-processing; c, data grouping; d, optimization searching of super parameters of a BP neural network and initialization; e, first-time training of the BP neural network; f, second-time training of the BP neural network; and g, training accomplishment of the BP neural network, the notch widths of the oak laser cutting system under different parameters are predicted. The method is advantaged in that over-concentrated and over-sparse data conditions are trained, and thetraining effect of the BP neural network is improved.

Description

technical field [0001] The invention relates to the field of product processing and manufacturing, in particular to a training and testing method of a BP neural network regression model and an application system thereof. Background technique [0002] Regression analysis is to determine the causal relationship between variables by specifying dependent variables and independent variables, establish a regression model, and solve each parameter of the model according to the measured data, and then evaluate whether the regression model can fit the measured data well; if it can If the fit is good, further predictions can be made based on the independent variables. [0003] In the field of product processing and manufacturing, experimental formulas or empirical formulas are generally used to roughly estimate the key parameters that need to be used in production and processing, and these experimental formulas or empirical formulas generally use regression analysis to determine the r...

Claims

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Application Information

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IPC IPC(8): G06N3/08G06K9/62
Inventor 陈达权黄运保李海艳
Owner GUANGDONG UNIV OF TECH
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