Abstract
| Language | English |
|---|---|
| Title of host publication | 22nd IMAC Conference and Exposition 2004 (IMAC XXII): A Conference and Exposition on Structural Dynamics |
| Pages | 1068-1076 |
| Number of pages | 9 |
| Publication status | Published - 2004 |
| Event | 22nd IMAC Conference and Exposition 2004 (IMAC XXII) - Dearborn, Michigan, United States Duration: 26 Jan 2004 → 29 Jan 2004 |
Conference
| Conference | 22nd IMAC Conference and Exposition 2004 (IMAC XXII) |
|---|---|
| Country | United States |
| City | Dearborn, Michigan |
| Period | 26/01/04 → 29/01/04 |
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Keywords
- information-gap
- robustness
- neural network
- regression model
Cite this
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Information-gap robustness of a neural network regression model. / Pierce, S.G.; Worden, K.; Manson, G.
22nd IMAC Conference and Exposition 2004 (IMAC XXII): A Conference and Exposition on Structural Dynamics. 2004. p. 1068-1076.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book
TY - GEN
T1 - Information-gap robustness of a neural network regression model
AU - Pierce, S.G.
AU - Worden, K.
AU - Manson, G.
PY - 2004
Y1 - 2004
N2 - As a result of their black-box nature, neural networks resist traditional methods of certification and therefore cannot be used in safety critical applications. This situation is undesirable as neural networks can provide an effective solution to many engineering problems. The object of the current paper is to explore the possibility of quantifying and qualifying the reliability of neural networks by a means outside the traditional framework. The approach used here will follow Ben-Haim’s information-gap theory of uncertainty. This is a non-probabilistic approach which may lend itself well to certification of black-box systems. The approach is demonstrated here on a neural network regression model of the process of pre-sliding friction between solids.
AB - As a result of their black-box nature, neural networks resist traditional methods of certification and therefore cannot be used in safety critical applications. This situation is undesirable as neural networks can provide an effective solution to many engineering problems. The object of the current paper is to explore the possibility of quantifying and qualifying the reliability of neural networks by a means outside the traditional framework. The approach used here will follow Ben-Haim’s information-gap theory of uncertainty. This is a non-probabilistic approach which may lend itself well to certification of black-box systems. The approach is demonstrated here on a neural network regression model of the process of pre-sliding friction between solids.
KW - information-gap
KW - robustness
KW - neural network
KW - regression model
UR - http://www.sem.org/Proceedings/ConferencePapers-Paper.cfm?ConfPapersPaperID=24505
M3 - Conference contribution book
SN - 9781604238020
SP - 1068
EP - 1076
BT - 22nd IMAC Conference and Exposition 2004 (IMAC XXII): A Conference and Exposition on Structural Dynamics
ER -