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Meta-Regression: A Framework for Robust Reactive Optimization

Authors :
McClary, Dan
Syrotiuk, Violet R.
Kulahci, Murat
McClary, Dan
Syrotiuk, Violet R.
Kulahci, Murat
Source :
McClary , D , Syrotiuk , V R & Kulahci , M 2007 , Meta-Regression: A Framework for Robust Reactive Optimization . in First IEEE International Conference on Self-Adaptive and Self-Organizing Systems . IEEE Computer Society Press , pp. 375-378 , First IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2007) , Boston, MA , United States , 09/07/2007 .
Publication Year :
2007

Abstract

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.

Details

Database :
OAIster
Journal :
McClary , D , Syrotiuk , V R & Kulahci , M 2007 , Meta-Regression: A Framework for Robust Reactive Optimization . in First IEEE International Conference on Self-Adaptive and Self-Organizing Systems . IEEE Computer Society Press , pp. 375-378 , First IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2007) , Boston, MA , United States , 09/07/2007 .
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.ocn842486959
Document Type :
Electronic Resource