Abstract
This paper presents a novel approach to capture emerging systems behavior involving multiple performance criteria. Due to the interactions that exist among systems, engineers may be faced with a multi-objective design space that current single response data mining models do not capture. We aim to address this challenge by proposing a Multi-Response Trend Mining algorithm that simultaneously predicts multiple performance objectives by identifying the time series behavior of the individual systems. The proposed approach is a departure from conventional data mining approaches that are often limited to evaluating single response variables in a given static data set. The resulting system level predictions will serve as performance targets for next generation systems design efforts. The Multi- Response Trend Mining model can then be integrated with multi-objective engineering models during the systems design and analysis phase so that engineering design solutions better reflect emerging system performance trends. A vehicle design data set from the UC Irvine Machine Learning Repository is used to validate the proposed methodology and highlight the need for multi-response predictive algorithms in systems design.
| Original language | English (US) |
|---|---|
| Title of host publication | 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010 |
| DOIs | |
| State | Published - Dec 1 2010 |
| Event | 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, MAO 2010 - Ft. Worth, TX, United States Duration: Sep 13 2010 → Sep 15 2010 |
Publication series
| Name | 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010 |
|---|
Other
| Other | 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, MAO 2010 |
|---|---|
| Country | United States |
| City | Ft. Worth, TX |
| Period | 9/13/10 → 9/15/10 |
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All Science Journal Classification (ASJC) codes
- Aerospace Engineering
- Mechanical Engineering
Cite this
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Capturing emergent behavior in multi-response systems through data trend mining. / Tucker, Conrad S.; Kim, Harrison M.
13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010. 2010. (13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Capturing emergent behavior in multi-response systems through data trend mining
AU - Tucker, Conrad S.
AU - Kim, Harrison M.
PY - 2010/12/1
Y1 - 2010/12/1
N2 - This paper presents a novel approach to capture emerging systems behavior involving multiple performance criteria. Due to the interactions that exist among systems, engineers may be faced with a multi-objective design space that current single response data mining models do not capture. We aim to address this challenge by proposing a Multi-Response Trend Mining algorithm that simultaneously predicts multiple performance objectives by identifying the time series behavior of the individual systems. The proposed approach is a departure from conventional data mining approaches that are often limited to evaluating single response variables in a given static data set. The resulting system level predictions will serve as performance targets for next generation systems design efforts. The Multi- Response Trend Mining model can then be integrated with multi-objective engineering models during the systems design and analysis phase so that engineering design solutions better reflect emerging system performance trends. A vehicle design data set from the UC Irvine Machine Learning Repository is used to validate the proposed methodology and highlight the need for multi-response predictive algorithms in systems design.
AB - This paper presents a novel approach to capture emerging systems behavior involving multiple performance criteria. Due to the interactions that exist among systems, engineers may be faced with a multi-objective design space that current single response data mining models do not capture. We aim to address this challenge by proposing a Multi-Response Trend Mining algorithm that simultaneously predicts multiple performance objectives by identifying the time series behavior of the individual systems. The proposed approach is a departure from conventional data mining approaches that are often limited to evaluating single response variables in a given static data set. The resulting system level predictions will serve as performance targets for next generation systems design efforts. The Multi- Response Trend Mining model can then be integrated with multi-objective engineering models during the systems design and analysis phase so that engineering design solutions better reflect emerging system performance trends. A vehicle design data set from the UC Irvine Machine Learning Repository is used to validate the proposed methodology and highlight the need for multi-response predictive algorithms in systems design.
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UR - http://www.scopus.com/inward/citedby.url?scp=84880816438&partnerID=8YFLogxK
U2 - 10.2514/6.2010-9322
DO - 10.2514/6.2010-9322
M3 - Conference contribution
AN - SCOPUS:84880816438
SN - 9781600869549
T3 - 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010
BT - 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010
ER -