Deviation classification and parameter optimization method based on least square support vector machine technology

A technology of support vector machine and least squares, applied in the direction of computer parts, instruments, characters and pattern recognition, etc., can solve the problems of large time consumption, achieve high classification accuracy, good promotion ability, and improve classification performance
CN103324939AActive Publication Date: 2013-09-25JIANGNAN UNIV +1
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
JIANGNAN UNIV
Publication Date
2013-09-25

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Abstract

The invention provides a deviation classification and parameter optimization method based on the least square support vector machine technology. A least square support vector machine is used for serving as a classifier, and is good in popularization capacity, and applicable to occasions with the requirement for high real-time performance, a virtual minority class oversampling algorithm is improved, influence of an isolating sample is eliminated, importance of a boundary sample is highlighted, classification can have a certain deviation, and the probability of wrongly classifying defective products into accepted products is reduced. According to the parameter optimization method based on the least square support vector machine technology, firstly, primary parameter optimization is carried out through a coupling simulated annealing algorithm, then sophisticated search is carried out by using a grid algorithm on the basis, the time for parameter optimization is reduced when a least square support vector machine model is trained, accuracy of classification is higher, and classification performance is improved.
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Description

technical field

[0001] The invention relates to the field of pattern recognition in machine vision detection, and specifically refers to a method for realizing biased classification of industrial products and a parameter optimization method of the least squares support vector machine through least squares support vector machine technology. Background technique

[0002] Machine vision technology is an important branch of computer science. After more than 30 years of rapid development, with its advantages of fast speed, high precision and never fatigue, it gradually replaces manual visual inspection on industrial production lines, reducing the While reducing labor costs, strict control of product quality can be achieved.

[0003] As an important field of machine vision, pattern recognition, a classifier with high classification accuracy and good real-time performance plays a vital role in the control of product qualification rate. In actual production, we should try our best ...

Claims

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