Moving target identification method of self-adaptive dynamic clustering least square support vector machine

A technology of support vector machine and dynamic clustering, applied in character and pattern recognition, computer components, instruments, etc., can solve problems such as limiting the promotion performance of LS-SVM

Pending Publication Date: 2021-12-07
YIBIN UNIV +1
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Problems solved by technology

[0006] Although LS-SVM has many advantages, its own inherent defects are also very prominent. This defect often limits the promotion per

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  • Moving target identification method of self-adaptive dynamic clustering least square support vector machine
  • Moving target identification method of self-adaptive dynamic clustering least square support vector machine
  • Moving target identification method of self-adaptive dynamic clustering least square support vector machine

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[0044] 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.

[0045] see figure 1 ~5, the embodiment of the present invention provides a moving target recognition method of an adaptive dynamic clustering least squares support vector machine, the method comprising:

[0046] Take the one-dimensional range image sample set of the moving target;

[0047] Using the second-order Renyi entropy between samples and the second-order Renyi entropy clustering accuracy β as clustering parameters, different clustering methods are use...

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Abstract

The invention discloses a moving target identification method of a self-adaptive dynamic clustering least square support vector machine. The method comprises the following steps: firstly, respectively clustering different types of training sample sets by utilizing a current most advanced self-adaptive dynamic clustering method, and then extracting the center of each cluster as a new training sample of the least square support vector machine, so as to achieve the purpose of training sample sparsification. According to the method, in a new sample sparsification method, no training sample is deleted, sparsification of the training sample is completed through self-adaptive dynamic clustering, the number of iterations is small, the calculated amount is small, generated accumulated errors become small, and hardware implementation is easy.

Description

technical field [0001] The invention relates to the technical field of machine learning, and more specifically relates to a moving target recognition method of an adaptive dynamic clustering least squares support vector machine. Background technique [0002] Data-based machine learning is a very important aspect of modern intelligent technology. It mainly studies how to start from some observation data (samples) to obtain laws that cannot be obtained through principle analysis at present, and use these laws to analyze objective objects and analyze future data. or unobservable data to make predictions. There are a large number of things in the real world that we can't accurately understand but can observe. Therefore, data-based machine learning has very important applications in various fields from modern science and technology to modern society and economy. When we abstract the laws to be studied into classification relations, the data-based machine learning problem is patt...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2321G06F18/23G06F18/2411
Inventor 李玉丽吴宗亮张涛张富麒李坤
Owner YIBIN UNIV
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