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Improving an Ergonomics Testing Procedure via Approximation-based Adaptive Experimental Design (2005)
| Content Provider | CiteSeerX |
|---|---|
| Author | Sasena, Michael J. Parkinson, Matthew Reed, Matthew P. Papalambros, Panos Y. Goovaerts, Pierre |
| Abstract | Adaptive design refers to experimental design where the next sample point is determined by information from previous experiments. This article presents a constrained optimization algorithm known as superEGO (a variant of the EGO algorithm of Schonlau, Welch, and Jones) that can create adaptive designs using kriging approximations. Our primary goal is to illustrate that superEGO is well-suited to generating adaptive designs which have many advantages over competing methods. The approach is demonstrated on a novel human-reach experiment where the selection of sampling points adapts to the individual test subject. Results indicate that superEGO is effective at satisfying the experimental objectives. �DOI: 10.1115/1.1906247� |
| File Format | |
| Journal | ASME J. Mech. Des |
| Language | English |
| Publisher Date | 2005-01-01 |
| Access Restriction | Open |
| Subject Keyword | Approximation-based Adaptive Experimental Design Ergonomics Testing Procedure Adaptive Design Next Sample Point Novel Human-reach Experiment Ego Algorithm Previous Experiment Many Advantage Experimental Objective Individual Test Subject Constrained Optimization Point Adapts Primary Goal Adaptive Design Refers |
| Content Type | Text |
| Resource Type | Article |
