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Fabric defect classification method based on improved extreme learning machine

An extremely fast learning machine and classification method technology, which is applied in the field of fabric defect classification based on an improved extremely fast learning machine, can solve the problems of poor generalization ability and large training error, and achieves reduction of dependencies, improvement of generalization ability, and realization of The effect of real-time accurate detection

Inactive Publication Date: 2016-02-17
DONGHUA UNIV
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

In single hidden layer forward neural network learning, the number of hidden units is directly related to the generalization ability of the network. Too many or too few hidden units will make the training error larger and the generalization ability poorer.

Method used

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  • Fabric defect classification method based on improved extreme learning machine
  • Fabric defect classification method based on improved extreme learning machine
  • Fabric defect classification method based on improved extreme learning machine

Examples

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Embodiment Construction

[0018] Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0019] In order to verify the effectiveness of the present invention, the present embodiment detects defects of 8 kinds of plain weave defect images: slub, broken warp, oil stain, weft broken, missing weft, missing warp, hanging warp, and weft shrinkage. There are 50 pictures for each defect, 20 pictures are used as training, and 30 pictures are used as test samples, and compared with ELM, OS-ELM, SAOS-ELM to verify the perform...

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Abstract

The invention relates to a fabric defect classification method based on an improved extreme learning machine. The method comprises the steps that a defect image of a training sample is preprocessed; adaptive wavelets are constructed for decomposition to detect fabric defects, and feature extraction is conducted through a multi-feature fusion method to obtain defect features; the defect features are classified, and in the classifying process, an online ELM algorithm is introduced, and online ELM pruning is conducted on hidden nodes through a sensitivity analysis method. By the adoption of the method, the deficiency of the overall processing mode of bulk data through an ELM is overcome, dependency of algorithm performance on the hidden nodes is reduced, and pruning of the hidden nodes is conducted based on sensitivity analysis.

Description

technical field [0001] The invention relates to the technical field of fabric defect classification, in particular to a fabric defect classification method based on an improved extremely fast learning machine. Background technique [0002] The ELM algorithm is proposed on the basis of a single hidden layer feed-forward neural network. Single hidden layer forward neural network is a special kind of forward neural network, which only contains one hidden layer forward neural network. The famous universal approximation theorem shows that a single hidden layer feedforward neural network can approximate a given continuous function with arbitrary precision. Based on this theoretical guarantee, the single hidden layer feedforward neural network has been widely studied both in theoretical analysis and engineering application. [0003] Compared with the traditional neural network, ELM has many advantages: the ELM algorithm has fast learning speed and good generalization ability. The...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/241
Inventor 马强陈亮任正云
Owner DONGHUA UNIV
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