Meta-regression: A framework for robust reactive optimization

Daniel W. McClary, Violet Syrotiuk, Murat Kulahci

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationFirst International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2007
Pages375-378
Number of pages4
DOIs
StatePublished - Dec 18 2007
EventFirst International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2007 - Cambridge, MA, United States
Duration: Jul 9 2007Jul 11 2007

Publication series

NameFirst International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2007

Other

OtherFirst International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2007
CountryUnited States
CityCambridge, MA
Period7/9/077/11/07

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ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

McClary, D. W., Syrotiuk, V., & Kulahci, M. (2007). Meta-regression: A framework for robust reactive optimization. In First International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2007 (pp. 375-378). [4274935] (First International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2007). https://doi.org/10.1109/SASO.2007.37