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Clustering sampling method based on kohonen neural network

A neural network and clustering technology, applied in the field of neural network, can solve problems such as inability to sample frame sampling, loss of representativeness of data structure, estimation deviation, etc.

Inactive Publication Date: 2019-11-15
COMMUNICATION UNIVERSITY OF CHINA
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Problems solved by technology

However, if multiple sampling frames are involved, we cannot take out each sampling frame for sampling separately, because there may be some hidden connections between the sampling frames, and sampling the sampling frames separately may cause the sample to affect the population. The data structure loses representativeness so that the estimation of the population is biased

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  • Clustering sampling method based on kohonen neural network
  • Clustering sampling method based on kohonen neural network
  • Clustering sampling method based on kohonen neural network

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Embodiment Construction

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0030] The embodiment of the present invention discloses an improved sampling algorithm based on the results of the kohonen neural network algorithm, which not only ensures that the sample points will not be concentrated in a certain type of enterprises in the whole, but also can extract the enterprises that really need to be investigated . Improve the "Pareto effect" of export enterprises and the influence of the limitations of traditional sampling methods on...

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Abstract

The invention discloses a clustering sampling method based on a kohonen neural network, and the method comprises the following specific steps: inputting data, and extracting attribute features; clustering kononen neural network according to the extracted attribute features; determining the total sample amount by utilizing relative errors; giving different sample quantities to each class accordingto the big class of the clustered samples and the attribute characteristics of the big class; and after the sample size of each category is determined, stratified sampling is carried out in each category, allocating corresponding weights and acquiring a final sampling sample. The situation that the sample points cannot be concentrated in a certain kind of enterprises in the whole is ensured, and enterprises which really need to be investigated can be extracted. The Pareto effect existing in an export enterprise and the influence of the limitation of a traditional sampling method on a samplingresult are improved; and most extracted enterprises are prevented from being enterprises with the same attribute characteristics.

Description

technical field [0001] The invention relates to the technical field of neural networks, and more specifically relates to a clustering sampling method based on a kohonen neural network. Background technique [0002] Technical trade measures (referred to as "Technical Trade Measures") actually come from the term "Technical Barriers to Trade (TBT)" in the WTO system. Technical and trade measures mainly refer to non-tariff measures. With the continuous development of the global economy, the role of tariffs in international goods is gradually decreasing. Instead, under the current international situation, the impact of technical trade measures on international trade is increasing day by day. , Has become an effective means for countries to achieve economic and political goals. In the process of specific implementation of technical and trade measures, technical regulations, standards, and conformity assessment procedures are mainly used to form the first barrier for foreign trade...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04
CPCG06Q40/10G06N3/047G06F18/23
Inventor 王妍卿枫陈云鹏檀雷雷胡菁樊珑
Owner COMMUNICATION UNIVERSITY OF CHINA
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