TY - GEN
T1 - Meta-regression
T2 - First International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2007
AU - McClary, Daniel W.
AU - Syrotiuk, Violet
AU - Kulahci, Murat
PY - 2007/12/18
Y1 - 2007/12/18
N2 - Maintaining optimal performance as the conditions of a system change is a challenging problem. To solve this problem, we present meta-regression, a general methodology for alleviating traditional difficulties in nonlinear regression modelling. Meta-regression allows for reactive optimization, in which system components self-organize to changing conditions in a manner that is robust, or affected minimally by other sources of variability. Meta-regression extends profiling, providing a methodology for model-building when there is incomplete knowledge of the mechanisms and interactions of a nonlinear system.
AB - Maintaining optimal performance as the conditions of a system change is a challenging problem. To solve this problem, we present meta-regression, a general methodology for alleviating traditional difficulties in nonlinear regression modelling. Meta-regression allows for reactive optimization, in which system components self-organize to changing conditions in a manner that is robust, or affected minimally by other sources of variability. Meta-regression extends profiling, providing a methodology for model-building when there is incomplete knowledge of the mechanisms and interactions of a nonlinear system.
UR - http://www.scopus.com/inward/record.url?scp=37049011206&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=37049011206&partnerID=8YFLogxK
U2 - 10.1109/SASO.2007.37
DO - 10.1109/SASO.2007.37
M3 - Conference contribution
AN - SCOPUS:37049011206
SN - 0769529062
SN - 9780769529066
T3 - First International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2007
SP - 375
EP - 378
BT - First International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2007
Y2 - 9 July 2007 through 11 July 2007
ER -