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Rapid coverage case library maintenance method

A case library and fast technology, applied in the direction of reasoning methods, neural learning methods, biological neural network models, etc., can solve problems such as weak system performance, achieve the effects of small time-consuming classification training, dynamic maintenance, and avoid black box problems

Pending Publication Date: 2021-12-10
ZHENJIANG COLLEGE
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, the CBR system needs to maintain and manage a large number of cases, and the complexity of time and space must be carefully considered, otherwise there may be a situation of "the larger the case base, the weaker the system performance", which leads to the "swamp". Problems”—the capacity and efficiency of the CBR system

Method used

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  • Rapid coverage case library maintenance method
  • Rapid coverage case library maintenance method

Examples

Experimental program
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Effect test

Embodiment 1

[0050] figure 1 It is a flow chart of the steps of the case library maintenance method applied to fast coverage described in the embodiment of the present invention. The example data comes from the UCI machine learning dataset "waveform" 5000 instances*21 dimensions*3 categories

[0051] Such as figure 1 As shown, the embodiment of the present invention provides a method for maintaining a rapidly covered case base, including the following steps:

[0052] Step S1. Obtain the case base information from the CBR application system and perform spatial expansion projection; the specific content and steps are:

[0053] Step S11. Obtain case base attribute dimension 21, quantity 4000, category information 3 from the case base system;

[0054] Step S12. Adding one dimension to the 21-dimensional input sample vector space to expand the dimension;

[0055] Step S13. Transform the input samples into a hyperspherical transformation of equal length; perform a spherical transformation of...

Embodiment 2

[0063] figure 1 It is a flow chart of the steps of the case library maintenance method applied to fast coverage described in the embodiment of the present invention. The example data comes from the UCI machine learning dataset "letter" 20000 instances*16 dimensions*26 categories.

[0064] Such as figure 1 As shown, the embodiment of the present invention provides a method for maintaining a rapidly covered case base, including the following steps:

[0065] Step S1. Obtain the case base information from the CBR application system and perform spatial expansion projection; the specific content and steps are:

[0066] Step S11. Obtain case base attribute dimension 16, quantity 16000, category information 26 from the case base system;

[0067] Step S12. Adding one dimension to the 16-dimensional input sample vector space to expand the dimension;

[0068] Step S13. Transform the input samples into a hyperspherical transformation of equal length; perform a spherical transformation...

Embodiment 3

[0076] figure 1 It is a flow chart of the steps for maintaining the case base for fast coverage described in the embodiment of the present invention. The sample data comes from the UCI machine learning data set "forest cover type" 581012 instances * 55 dimensions * 7 categories, and the experiment tests the dynamic maintenance of a large-scale case library.

[0077] Such as figure 1 As shown, the embodiment of the present invention provides a method for maintaining a rapidly covered case base, including the following steps:

[0078] Step S1. Obtain the case base information from the CBR application system and perform spatial expansion projection; the specific content and steps are:

[0079] Step S11. Obtain case base attribute dimension 55 and category information 7 from the case base system, and the quantities are 10,000, 50,000, and 100,000 respectively;

[0080] Step S12. Adding one dimension to the 55-dimensional input sample vector space to expand the dimension;

[00...

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Abstract

The invention discloses a rapid coverage case library maintenance method. The method comprises the following steps: firstly, acquiring case library information from a CBR application system and carrying out spatial dimension expansion projection; dividing a case library space according to the similarity to obtain a coverage field and sub-classifications; and constructing a three-layer feed-forward neural network to realize quick recall of the most similar case. The three-layer feedforward neural network which is easy to construct and understand is adopted, the algorithm complexity of the neural network is effectively reduced through the domain coverage algorithm, and the operation capacity and efficiency of the CBR system are ensured.

Description

technical field [0001] The invention belongs to a case base maintenance method (CBM) based on case reasoning (Case-Based Reasoning, CBR), and particularly relates to the performance maintenance of a large-scale irreducible case base. Background technique [0002] CBR comes from the analogical reasoning method of human cognition. A case is a piece of knowledge with context information. The case base is the main knowledge base in the CBR system; its learning function is to continuously add new cases to the case base. The case base Each of the cases in has the potential to be adapted to solve future problems. Generally speaking, the larger the case base and the richer the knowledge, the more it can reflect the intelligence level of the system. [0003] As an important machine learning method, the case base is the core knowledge base of the CBR reasoning system, but it is difficult to maintain. One of the main factors is that the case base is large, unstructured or semi-structu...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N5/04G06N3/04G06N3/08G06K9/62
CPCG06N5/04G06N3/08G06N3/045G06F18/22G06F18/24
Inventor 李建洋吴宏森吴辉
Owner ZHENJIANG COLLEGE
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