Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine
This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output res...
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Hindawi Limited
2014-01-01
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Series: | Computational Intelligence and Neuroscience |
Online Access: | http://dx.doi.org/10.1155/2014/740838 |
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doaj-96399ca2bf364feb9fe6f2f9b918283e2020-11-24T22:55:54ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52651687-52732014-01-01201410.1155/2014/740838740838Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning MachineJingyu Zhou0Shulin Tian1Chenglin Yang2Xuelong Ren3School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaThis paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation algorithm proposed in this paper contains mainly three aspects of innovation. Firstly, this algorithm saves time efficiently by classifying response space with ELM. Secondly, this algorithm can avoid reduced test precision efficiently in case of reduction of the number of impulse-response samples. Thirdly, a new process of test signal generator and a test structure in test generation algorithm are presented, and both of them are very simple. Finally, the abovementioned improvement and functioning are confirmed in experiments.http://dx.doi.org/10.1155/2014/740838 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jingyu Zhou Shulin Tian Chenglin Yang Xuelong Ren |
spellingShingle |
Jingyu Zhou Shulin Tian Chenglin Yang Xuelong Ren Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine Computational Intelligence and Neuroscience |
author_facet |
Jingyu Zhou Shulin Tian Chenglin Yang Xuelong Ren |
author_sort |
Jingyu Zhou |
title |
Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine |
title_short |
Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine |
title_full |
Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine |
title_fullStr |
Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine |
title_full_unstemmed |
Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine |
title_sort |
test generation algorithm for fault detection of analog circuits based on extreme learning machine |
publisher |
Hindawi Limited |
series |
Computational Intelligence and Neuroscience |
issn |
1687-5265 1687-5273 |
publishDate |
2014-01-01 |
description |
This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation algorithm proposed in this paper contains mainly three aspects of innovation. Firstly, this algorithm saves time efficiently by classifying response space with ELM. Secondly, this algorithm can avoid reduced test precision efficiently in case of reduction of the number of impulse-response samples. Thirdly, a new process of test signal generator and a test structure in test generation algorithm are presented, and both of them are very simple. Finally, the abovementioned improvement and functioning are confirmed in experiments. |
url |
http://dx.doi.org/10.1155/2014/740838 |
work_keys_str_mv |
AT jingyuzhou testgenerationalgorithmforfaultdetectionofanalogcircuitsbasedonextremelearningmachine AT shulintian testgenerationalgorithmforfaultdetectionofanalogcircuitsbasedonextremelearningmachine AT chenglinyang testgenerationalgorithmforfaultdetectionofanalogcircuitsbasedonextremelearningmachine AT xuelongren testgenerationalgorithmforfaultdetectionofanalogcircuitsbasedonextremelearningmachine |
_version_ |
1725656010992910336 |