A new design method of grey relational classifier

A technology of correlation classification and design method, which is applied in the direction of instruments, calculations, computer parts, etc., can solve problems such as poor anti-noise ability, low signal recognition rate of loaded signal-to-noise ratio, and slope correlation degree that does not meet the normative requirements.

Active Publication Date: 2020-07-14
SHANGHAI DIANJI UNIV
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Problems solved by technology

For the traditional Deng's gray relational classifier, the anti-noise ability is relatively poor, and it is difficult to achieve the recognition effect under the condition of low signal-to-noise ratio; the T-type correlation degree uses the speed ratio to reflect the development trend of the two sequences, and the slope correlation degree is The speed difference is used to reflect the similarity of the development trend of the two sequences or the shape of the curve. However, the dimensionless process of the original data actually changes the proportion of the curve, so the slope correlation degree does not meet the standardization; the B-type correlation degree comprehensively utilizes Displacement difference, velocity difference, acceleration difference to reflect the similarity and similarity of the two sequence curves, focusing on the overall analysis
Moreover, the recognition rate of the above models for signals loaded with signal-to-noise ratio is not high

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  • A new design method of grey relational classifier
  • A new design method of grey relational classifier
  • A new design method of grey relational classifier

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Embodiment Construction

[0043] In order to make the content of the present invention clearer and easier to understand, the content of the present invention will be described in detail below in conjunction with specific embodiments and accompanying drawings.

[0044] The invention strengthens the standardization and self-adaptive ability of the gray relational classifier, and first proposes a design method of the common gray relational classifier. Then normalize and average the initial value image, and strengthen the constraint ability on the resolution coefficient. Finally, the entropy weight algorithm can be used to improve its self-adaptive ability, and a technical method of a new gray relational classifier is proposed.

[0045] The basic idea of ​​gray relational theory is to quantitatively describe and compare the changes and development trend of a system. Suppose the behavior sequence of the system is:

[0046] x 0 =(x 0 (1),x 0 (2),...,x 0 (n))

[0047] x 1 =(x 1 (1),x 1 (2),...,x 1 ...

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Abstract

The present invention provides a novel gray relational classifier design method, comprising: designing a gray relational classifier using a gray relational algorithm; normalizing and averaging the initial value images of each sequence of behavior of the system, and assigning different signals to different signals. The resolution coefficient, so that the self-adaptive resolution coefficient is used to strengthen the constraint ability of the resolution coefficient.

Description

technical field [0001] The invention relates to the fields of electronic countermeasures, signal recognition, and classifier design, and more specifically, the invention relates to a design method of a novel gray relational classifier. Background technique [0002] Today, with the continuous improvement of anti-reconnaissance and anti-interference technology, the complexity of communication systems and the continuous increase of noise, the individual signal differences are gradually reduced, and the traditional template comparison method has been difficult to complete the task of individual identification of radiation sources. How to realize the identification and classification of radiation source signals in the environment of low and unstable signal-to-noise ratio, the design and selection of classifiers become very important. The main function of classifier design is to make corresponding judgments according to the extracted signal features, so as to realize the classific...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06K9/00
CPCG06F2218/12G06F18/24133
Inventor 王生李靖超冯云鹤曹曼琳
Owner SHANGHAI DIANJI UNIV
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