Sequential optimization through adaptive design of experiments

This thesis considers the problem of achieving better system performance through adaptive experiments. For the case of discrete design space, I propose an adaptive One-Factor-at-A-Time (OFAT) experimental design, study its properties and compare its performance to saturated fractional factorial designs. The rationale for adopting the adaptive OFAT design scheme become clear if it is imbedded in a Bayesian framework: it becomes clear that OFAT is an efficient response to step by step accrual of sample information. The Bayesian predictive distribution for the outcome by implementing OFAT and the corresponding principal moments when a natural conjugate prior is assigned to parameters that are not known with certainty are also derived. For the case of compact design space, I expand the treatment of OFAT by the removal of two restrictions imposed on the discrete design space. The first is that the selection of input level at each iteration depends only on observed best response and does not depend on other prior information. In most real cases, domain experts possess knowledge about the process being modeled that, ideally, should be treated as sample information in its own right-and not simply ignored.

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering Systems Division.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2007

Author dc:creator

• Wang, Hungjen, 1971-

Advisor dc:contributor.advisor

• Daniel D. Frey, Gordon M. Kaufman and Roy E. Welsch.

• Engineering Systems Division.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/39332

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/39332