An automatic inspection system based on a neural network and uniform design

Meng Xin Li; Cheng Dong Wu; Yong Yue

An automatic inspection system based on a neural network and uniform design

Meng Xin Li^*, Cheng Dong Wu, Yong Yue

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

1 Citation (Scopus)

Abstract

To solve the shortcomings of the traditional BP network, the improved algorithm is presented to accelerate the training and improve the accuracy, and reduce the possibility of getting into the local minimum. For optimal network structure, the UD method is introduced to optimise the parameters, and the 'best' level-combination is obtained so that the performance of the classifier is further improved

Original language	English
Title of host publication	2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Pages	4528-4532
Number of pages	5
Publication status	Published - 2005
Externally published	Yes
Event	International Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China Duration: 18 Aug 2005 → 21 Aug 2005

Publication series

Name	2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

Conference

Conference	International Conference on Machine Learning and Cybernetics, ICMLC 2005
Country/Territory	China
City	Guangzhou
Period	18/08/05 → 21/08/05

Keywords

Defect inspection
Parameter optimization
The improved BP algorithm
Uniform design

Cite this

@inproceedings{55e591e2cdd84bbe825fd77f371336b1,

title = "An automatic inspection system based on a neural network and uniform design",

abstract = "To solve the shortcomings of the traditional BP network, the improved algorithm is presented to accelerate the training and improve the accuracy, and reduce the possibility of getting into the local minimum. For optimal network structure, the UD method is introduced to optimise the parameters, and the 'best' level-combination is obtained so that the performance of the classifier is further improved",

keywords = "Defect inspection, Parameter optimization, The improved BP algorithm, Uniform design",

author = "Li, {Meng Xin} and Wu, {Cheng Dong} and Yong Yue",

year = "2005",

language = "English",

isbn = "078039092X",

series = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",

pages = "4528--4532",

booktitle = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",

note = "International Conference on Machine Learning and Cybernetics, ICMLC 2005 ; Conference date: 18-08-2005 Through 21-08-2005",

}

Li, MX, Wu, CD & Yue, Y 2005, An automatic inspection system based on a neural network and uniform design. in 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005. 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005, pp. 4528-4532, International Conference on Machine Learning and Cybernetics, ICMLC 2005, Guangzhou, China, 18/08/05.

An automatic inspection system based on a neural network and uniform design. / Li, Meng Xin; Wu, Cheng Dong; Yue, Yong.
2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005. 2005. p. 4528-4532 (2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - An automatic inspection system based on a neural network and uniform design

AU - Li, Meng Xin

AU - Wu, Cheng Dong

AU - Yue, Yong

PY - 2005

Y1 - 2005

N2 - To solve the shortcomings of the traditional BP network, the improved algorithm is presented to accelerate the training and improve the accuracy, and reduce the possibility of getting into the local minimum. For optimal network structure, the UD method is introduced to optimise the parameters, and the 'best' level-combination is obtained so that the performance of the classifier is further improved

AB - To solve the shortcomings of the traditional BP network, the improved algorithm is presented to accelerate the training and improve the accuracy, and reduce the possibility of getting into the local minimum. For optimal network structure, the UD method is introduced to optimise the parameters, and the 'best' level-combination is obtained so that the performance of the classifier is further improved

KW - Defect inspection

KW - Parameter optimization

KW - The improved BP algorithm

KW - Uniform design

UR - http://www.scopus.com/inward/record.url?scp=28444466981&partnerID=8YFLogxK

M3 - Conference Proceeding

AN - SCOPUS:28444466981

SN - 078039092X

SN - 9780780390928

T3 - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

SP - 4528

EP - 4532

BT - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

T2 - International Conference on Machine Learning and Cybernetics, ICMLC 2005

Y2 - 18 August 2005 through 21 August 2005

ER -

An automatic inspection system based on a neural network and uniform design

Abstract

Publication series

Conference

Keywords

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Cite this