Method for forecasting the commodity barcode registration quantity

A BP neural network and product barcode technology, applied in the field of time series prediction, can solve the problems of little in-depth research and the number of product barcode registrations staying in the statistical stage

Inactive Publication Date: 2017-01-25
SOUTH CHINA UNIV OF TECH
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  • Abstract
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Problems solved by technology

[0003] At present, the research on the registration volume of commodity barcodes only stays at the statistical stage, and there is little further in-depth research

Method used

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  • Method for forecasting the commodity barcode registration quantity
  • Method for forecasting the commodity barcode registration quantity
  • Method for forecasting the commodity barcode registration quantity

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Embodiment

[0040] attached figure 1 shown, with figure 1 It is a flow chart of the method for predicting the registration quantity of commodity barcodes based on BP neural network disclosed by the present invention. figure 1 Specifically illustrate a kind of commodity barcode registration quantity prediction method based on BP neural network, this method comprises the following steps:

[0041] Step S1, taking the registration time of the commodity barcode registration information as a statistical item to make statistics, obtain the commodity barcode registration volume sequence, generate training samples by analyzing the autocorrelation characteristics of the commodity barcode registration volume sequence, and perform normalization processing;

[0042] In this embodiment, the commodity barcode registration quantity sequence is the monthly commodity barcode registration quantity sequence, and the commodity barcode registration information is counted on a monthly basis, that is, the regis...

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Abstract

The invention discloses a method for forecasting the commodity barcode registration quantity based on BP neural network. The method comprises the following steps: analyzing the autocorrelation analysis chart of the commodity barcode registration quantity and generating the training sample; establishing a BP neural network, and using the generated training sample for training, so as to obtain a forecasting model of commodity barcode registration quantity; forecasting the commodity barcode registration quantity by using the forecasting model. The method takes into account the non-linear characteristics of the data of commodity barcode registration quantity, and enables high-precision prediction of the commodity barcode registration quantity. The method adopts the single hidden layer BP neural network under the premise of ensuring high prediction accuracy, which greatly reduces the computational difficulty and complexity compared with the multi-hidden layer neural network. The method is characterized by simple operation.

Description

technical field [0001] The invention relates to the technical field of time series prediction, in particular to a method for predicting the registration volume of commodity barcodes based on BP neural network. Background technique [0002] Commodity barcode registration information covers enterprise name, administrative division, registered capital, enterprise category, economic type code, national economic industry classification code, last operation time, whether to cancel, registration date, etc., and the registration date is used as the statistical item for statistics. Obtain the monthly product barcode registration volume data. The number of product barcode registrations reflects the manufacturing and sales of new products. From a macro perspective, changes in the number of product barcode registrations within a certain period of time can directly reflect the performance of product manufacturing and sales in a region. It can indirectly reflect the activity of commercia...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/08
CPCG06N3/084G06Q10/04
Inventor 谢巍邓智敏杨晓峰
Owner SOUTH CHINA UNIV OF TECH
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