TY - GEN
T1 - Adaptive experimental design applied to an ergonomics testing procedure
AU - Sasena, Michael
AU - Parkinson, Matthew
AU - Goovaerts, Pierre
AU - Papalambros, Panos
AU - Reed, Matthew
N1 - Funding Information:
This research has been partially supported by the Automotive Research Center at the University of Michigan, a US Army Center of Excellence in Modeling and Simulation of Ground Vehicles and by the General Motors Collaborative Research Laboratory at the University of Michigan. This support is gratefully acknowledged. The authors would also like to thank the two test subjects for their patience and cooperation.
PY - 2002
Y1 - 2002
N2 - Nonlinear constrained optimization algorithms are widely utilized in artifact design. Certain algorithms also lend themselves well to design of experiments (DOE). Adaptive design refers to experimental design where determining where to sample next is influenced by information from previous experiments. We present a constrained optimization algorithm known as superEGO (a variant of the EGO algorithm of Schonlau, Welch and Jones) that is able to create adaptive designs effectively. Its ability to allow easily for a variety of sampling criteria and to incorporate constraint information accurately makes it well suited to the needs of adaptive design. The approach is demonstrated on a human reach experiment where the selection of sampling points adapts successfully to the stature and perception of the individual test subject. Results from the initial study indicate that superEGO is able to create experimental designs that yield more accurate models using fewer points than the original testing procedure.
AB - Nonlinear constrained optimization algorithms are widely utilized in artifact design. Certain algorithms also lend themselves well to design of experiments (DOE). Adaptive design refers to experimental design where determining where to sample next is influenced by information from previous experiments. We present a constrained optimization algorithm known as superEGO (a variant of the EGO algorithm of Schonlau, Welch and Jones) that is able to create adaptive designs effectively. Its ability to allow easily for a variety of sampling criteria and to incorporate constraint information accurately makes it well suited to the needs of adaptive design. The approach is demonstrated on a human reach experiment where the selection of sampling points adapts successfully to the stature and perception of the individual test subject. Results from the initial study indicate that superEGO is able to create experimental designs that yield more accurate models using fewer points than the original testing procedure.
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M3 - Conference contribution
AN - SCOPUS:0036974206
SN - 0791836215
SN - 9780791836217
T3 - ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE2002
SP - 529
EP - 537
BT - ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE2002
T2 - ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE2002
Y2 - 29 September 2002 through 2 October 2002
ER -